Showing posts with label Deep Learning. Show all posts
Showing posts with label Deep Learning. Show all posts
Cutting-Edge AI: Deep Reinforcement Learning in Python
Wednesday, October 22, 2025
Free Coupon Discount - Cutting-Edge AI: Deep Reinforcement Learning in Python, Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG
- Highest Rated
- Created by Lazy Programmer Inc.
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Description
Welcome to Cutting-Edge AI!
This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course.
Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks).
While both of these have been around for quite some time, it’s only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning.
The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.
Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.
We’ve seen how AlphaZero can master the game of Go using only self-play.
This is just a few years after the original AlphaGo already beat a world champion in Go.
We’ve seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation.
Simulation is nice because it doesn’t require actual hardware, which is expensive. If your agent falls down, no real damage is done.
We’ve seen real-world robots learn hand dexterity, which is no small feat.
Walking is one thing, but that involves coarse movements. Hand dexterity is complex - you have many degrees of freedom and many of the forces involved are extremely subtle.
Imagine using your foot to do something you usually do with your hand, and you immediately understand why this would be difficult.
Last but not least - video games.
Even just considering the past few months, we’ve seen some amazing developments. AIs are now beating professional players in CS:GO and Dota 2.
So what makes this course different from the first two?
Now that we know deep learning works with reinforcement learning, the question becomes: how do we improve these algorithms?
This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies.
Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more "black box" approach, inspired by biological evolution.
What’s also great about this new course is the variety of environments we get to look at.
First, we’re going to look at the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone.
Second, we’re going to look at MuJoCo, which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics - we first have to show it can work with simulated physics.
Finally, we’re going to look at Flappy Bird, everyone’s favorite mobile game just a few years ago.
Thanks for reading, and I’ll see you in class!
Suggested prerequisites:
Calculus
Probability
Object-oriented programming
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations
Linear regression
Gradient descent
Know how to build a convolutional neural network (CNN) in TensorFlow
Markov Decision Proccesses (MDPs)
TIPS (for getting through the course):
Watch it at 2x.
Take handwritten notes. This will drastically increase your ability to retain the information.
Write down the equations. If you don't, I guarantee it will just look like gibberish.
Ask lots of questions on the discussion board. The more the better!
Realize that most exercises will take you days or weeks to complete.
Write code yourself, don't just sit there and look at my code.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)
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Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)
Monday, August 25, 2025
Free Coupon Discount - VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python | Created by Lazy Programmer Inc.
Students also bought
- Advanced AI: Deep Reinforcement Learning in Python
- Deep Learning: Convolutional Neural Networks in Python
- Cutting-Edge AI: Deep Reinforcement Learning in Python
- Complete Guide to TensorFlow for Deep Learning with Python
- PyTorch for Deep Learning with Python Bootcamp
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Description
Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.
This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.
When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.
I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.
Let me give you a quick rundown of what this course is all about:
We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!)
We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.
In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.
You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)
We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.
Another very popular computer vision task that makes use of CNNs is called neural style transfer.
This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.
I will also introduce you to the now-famous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images.
Currently, we also implement object localization, which is an essential first step toward implementing a full object detection system.
I hope you’re excited to learn about these advanced applications of CNNs, I’ll see you in class!
AWESOME FACTS:
One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.
Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.
Another result? No complicated low-level code such as that written in Tensorflow, Theano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.
Suggested Prerequisites:
Know how to build, train, and use a CNN using some library (preferably in Python)
Understand basic theoretical concepts behind convolution and neural networks
Decent Python coding skills, preferably in data science and the Numpy Stack
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)
Who this course is for:
Students and professionals who want to take their knowledge of computer vision and deep learning to the next level
Anyone who wants to learn about object detection algorithms like SSD and YOLO
Anyone who wants to learn how to write code for neural style transfer
Anyone who wants to use transfer learning
Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast
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[2025] Tensorflow 2: Deep Learning & Artificial Intelligence
Friday, August 22, 2025
Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!
Unlock the Power of TensorFlow 2.0: Elevate Your Deep Learning and AI Skills
Are you ready to dive into the transformative world of artificial intelligence? TensorFlow 2.0 is here to revolutionize how we build and deploy machine learning models. With its enhanced features and user-friendly interface, TensorFlow 2.0 is a game-changer for both newcomers and seasoned data scientists.
Why TensorFlow 2.0? The latest version of TensorFlow brings significant improvements that simplify the development of deep learning models. It integrates seamlessly with Keras, offering an intuitive high-level API for rapid experimentation and model building. Whether you're working on computer vision, natural language processing, or predictive analytics, TensorFlow 2.0 provides the tools you need to create powerful AI solutions.
What You'll Learn: Our comprehensive course on TensorFlow 2.0 is designed to equip you with practical skills and hands-on experience. You’ll start with the fundamentals of TensorFlow 2.0, exploring its new features and capabilities. As you progress, you’ll delve into building and training neural networks, optimizing models for performance, and deploying AI solutions in real-world scenarios.
Why Enroll? By joining our TensorFlow 2.0 course, you’ll gain a competitive edge in the rapidly evolving field of AI. You'll learn from industry experts, engage in practical exercises, and work on real-world projects. Plus, our course is structured to fit your busy schedule, with flexible online access and support from experienced instructors.
Get Started Today! Don’t miss out on the opportunity to enhance your deep learning and AI skills with TensorFlow 2.0. Sign up now and take the first step towards mastering the future of technology!
A deep understanding of deep learning (with Python intro)
Sunday, January 5, 2025
Free Udemy Coupon
A deep understanding of deep learning (with Python intro) - Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.
- Highest Rated
- Created by Mike X Cohen
- English [Auto]
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What you'll learn
- The theory and math underlying deep learning
- How to build artificial neural networks
- Architectures of feedforward and convolutional networks
- Building models in PyTorch
- The calculus and code of gradient descent
- Fine-tuning deep network models
- Learn Python from scratch (no prior coding experience necessary)
- How and why autoencoders work
- How to use transfer learning
- Improving model performance using regularization
- Optimizing weight initializations
- Understand image convolution using predefined and learned kernels
- Whether deep learning models are understandable or mysterious black-boxes!
- Using GPUs for deep learning (much faster than CPUs!)
Description
Deep learning is increasingly dominating technology and has major implications for society.
From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology.
But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.
Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.
How does deep learning work?
Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data.
Is it really that simple? mmm OK, it's actually a tiny bit more complicated than that ;) but that's the core idea, and everything else -- literally everything else in deep learning -- is just clever ways of putting together these fundamental building blocks. That doesn't mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks.
Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning -- I mean, really learn deep learning, not just have superficial knowledge from a youtube video -- by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport!
What is this course all about?
Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future.
Please note: This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems.
You can learn everything about deep learning in this course.
In this course, you will learn
Theory: Why are deep learning models built the way they are?
Math: What are the formulas and mechanisms of deep learning?
Implementation: How are deep learning models actually constructed in Python (using the PyTorch library)?
Intuition: Why is this or that metaparameter the right choice? How to interpret the effects of regularization? etc.
Python: If you're completely new to Python, go through the 8+ hour coding tutorial appendix. If you're already a knowledgeable coder, then you'll still learn some new tricks and code optimizations.
Google-colab: Colab is an amazing online tool for running Python code, simulations, and heavy computations using Google's cloud services. No need to install anything on your computer.
Unique aspects of this course
Clear and comprehensible explanations of concepts in deep learning.
Several distinct explanations of the same ideas, which is a proven technique for learning.
Visualizations using graphs, numbers, and spaces that provide intuition of artificial neural networks.
LOTS of exercises, projects, code-challenges, suggestions for exploring the code. You learn best by doing it yourself!
Active Q&A forum where you can ask questions, get feedback, and contribute to the community.
8+ hour Python tutorial. That means you don't need to master Python before enrolling in this course.
So what are you waiting for??
Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.
I hope to see you soon in the course!
Mike
Who this course is for:
- Students in a deep learning course
- Machine-learning enthusiasts
- Anyone interested in mechanisms of AI (artificial intelligence)
- Data scientists who want to expend their library of skills
- Aspiring data scientists
- Scientists and researchers interested in deep learning
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Deep Learning Prerequisites: Linear Regression in Python
Monday, September 16, 2024
Free Coupon Discount - Deep Learning Prerequisites: Linear Regression in Python, Data science: Learn linear regression from scratch and build your own working program in Python for data analysis.
BESTSELLER, 4.6 (3,619 ratings), Created by Lazy Programmer Inc.
REVIEW THIS COURSE - GET COUPON CODE
What you'll learn
- Derive and solve a linear regression model, and apply it appropriately to data science problems
- Program your own version of a linear regression model in Python
Description
This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.
Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:
deep learning
machine learning
data science
statistics
In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.
What's that you say? Moore's Law is not linear?
You are correct! I will show you how linear regression can still be applied.
In the next section, we will extend 1-D linear regression to any-dimensional linear regression - in other words, how to create a machine learning model that can learn from multiple inputs.
We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight.
Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on.
This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE.
If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or "hacker", this course may be useful.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
calculus (taking derivatives)
matrix arithmetic
probability
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Who this course is for:
People who are interested in data science, machine learning, statistics and artificial intelligence
People new to data science who would like an easy introduction to the topic
People who wish to advance their career by getting into one of technology's trending fields, data science
Self-taught programmers who want to improve their computer science theoretical skills
Analytics experts who want to learn the theoretical basis behind one of statistics' most-used algorithms
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PyTorch: Deep Learning and Artificial Intelligence
Saturday, August 3, 2024
PyTorch: Deep Learning and Artificial Intelligence - Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
- Bestseller
- Created by Lazy Programmer Team, Lazy Programmer Inc.
- English [Auto]
What you'll learn
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
- Predict Stock Returns
- Time Series Forecasting
- Computer Vision
- How to build a Deep Reinforcement Learning Stock Trading Bot
- GANs (Generative Adversarial Networks)
- Recommender Systems
- Image Recognition
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Natural Language Processing (NLP) with Deep Learning
- Demonstrate Moore's Law using Code
- Transfer Learning to create state-of-the-art image classifiers
Description
Welcome to PyTorch: Deep Learning and Artificial Intelligence!
Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.
Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?
Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?
It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. ;)
On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.
If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.
Deep Learning has been responsible for some amazing achievements recently, such as:
Generating beautiful, photo-realistic images of people and things that never existed (GANs)
Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
Self-driving cars (Computer Vision)
Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)
This course is for beginner-level students all the way up to expert-level students. How can this be?
If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.
Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).
Current projects include:
Natural Language Processing (NLP)
Recommender Systems
Transfer Learning for Computer Vision
Generative Adversarial Networks (GANs)
Deep Reinforcement Learning Stock Trading Bot
Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses PyTorch, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.
This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).
I'm taking the approach that even if you are not 100% comfortable with the mathematical concepts, you can still do this! In this course, we focus more on the PyTorch library, rather than deriving any mathematical equations. I have tons of courses for that already, so there is no need to repeat that here.
Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.
Thanks for reading, and I’ll see you in class!
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Who this course is for:
Beginners to advanced students who want to learn about deep learning and AI in PyTorch
Deep Learning Prerequisites: Logistic Regression in Python
Thursday, March 9, 2023
Deep Learning Prerequisites: Logistic Regression in Python - Data science, machine learning, and artificial intelligence in Python for students and professionals
- Bestseller
- Created by Lazy Programmer Inc.
- English [Auto], Portuguese [Auto]
What you'll learn
- program logistic regression from scratch in Python
- describe how logistic regression is useful in data science
- derive the error and update rule for logistic regression
- understand how logistic regression works as an analogy for the biological neuron
- use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition
- understand why regularization is used in machine learning
Description
This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python.
This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.
This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.
Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!
If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
calculus (taking derivatives)
matrix arithmetic
probability
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Who this course is for:
Adult learners who want to get into the field of data science and big data
Students who are thinking of pursuing machine learning or data science
Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python
People who know some machine learning but want to be able to relate it to artificial intelligence
People who are interested in bridging the gap between computational neuroscience and machine learning
Deep Learning A-Z™: Hands-On Artificial Neural Networks
Sunday, March 5, 2023
- BESTSELLER
- 4.6 (25,470 ratings)
- Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
- English, French [Auto-generated], 4 more
Preview this Udemy Course - GET COUPON CODE
What you'll learn
- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
Description
*** As seen on Kickstarter ***
Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role.
But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.
--- Why Deep Learning A-Z? ---
Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there:
1. ROBUST STRUCTURE
The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it.
That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning.
2. INTUITION TUTORIALS
So many courses and books just bombard you with the theory, and math, and coding... But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms.
With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer.
3. EXCITING PROJECTS
Are you tired of courses based on over-used, outdated data sets?
Yes? Well then you're in for a treat.
Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:
Artificial Neural Networks to solve a Customer Churn problem
Convolutional Neural Networks for Image Recognition
Recurrent Neural Networks to predict Stock Prices
Self-Organizing Maps to investigate Fraud
Boltzmann Machines to create a Recomender System
Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize
*Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth.
4. HANDS-ON CODING
In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.
In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after.
This is a course which naturally extends into your career.
5. IN-COURSE SUPPORT
Have you ever taken a course or read a book where you have questions but cannot reach the author?
Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help.
In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum.
No matter how complex your query, we will be there. The bottom line is we want you to succeed.
--- The Tools ---
Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course you will learn both!
TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more.
PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook.
So which is better and for what?
Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances.
The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques.
--- More Tools ---
Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it.
Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing.
--- Even More Tools ---
Scikit-learn the most practical Machine Learning library. We will mainly use it:
to evaluate the performance of our models with the most relevant technique, k-Fold Cross Validation
to improve our models with effective Parameter Tuning
to preprocess our data, so that our models can learn in the best conditions
And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience.
Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently.
--- Who Is This Course For? ---
As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you're done with Deep Learning A-Z™ your skills are on the cutting edge of today's technology.
If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.
If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications.
--- Real-World Case Studies ---
Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges:
#1 Churn Modelling Problem
In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.
Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach.
If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn.
#2 Image Recognition
In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it - by simply changing the pictures in the input folder.
For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog!
#3 Stock Price Prediction
In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have long-term memory, just like us, humans.
The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course!
In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them.
#4 Fraud Detection
According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course.
This is the first part of Volume 2 - Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card.
This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications.
#5 & 6 Recommender Systems
From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet.
We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”.
Your final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models.
Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of.
And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila! The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix!
--- Summary ---
In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and real-World case studies.
We are super enthusiastic about Deep Learning and hope to see you inside the class!
Kirill & Hadelin
Who this course is for:
Anyone interested in Deep Learning
Students who have at least high school knowledge in math and who want to start learning Deep Learning
Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning
Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
Any students in college who want to start a career in Data Science
Any data analysts who want to level up in Deep Learning
Any people who are not satisfied with their job and who want to become a Data Scientist
Any people who want to create added value to their business by using powerful Deep Learning tools
Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business
Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms
Deep Learning for Beginner (AI) - Data Science
Monday, October 31, 2022
Deep Learning for Beginner (AI) - Data Science - Deep Learning for beginner, Mathematical & Graphical explanation of deep learning with ebooks and Python projects
New | Created by Moein Ud Din
Learn Deep Learning from scratch. It is the extension of a Machine Learning, this course is for beginner who wants to learn the fundamental of deep learning and artificial intelligence. The course includes video explanation with introductions (basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It's highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.
What you'll learn
- Introduction to Deep learning, resemblance of artificial neural network and biological neural network
- Activation function and its types, Application of activation function, Linear activation function, Non-linear activation function
- Types of activation function: Step function, Sign function, Linear function, ReLU function, Leaky ReLU function, Tangent Hyperbolic function, Sigmoid, Softmax
- Artificial neural network, ANN model, Complex ANN model, Labelled ANN model, Forward ANN, Backward ANN, ANN python project
- Convolutional Neural Network (CNN), CNN block diagram, Filter or Kernel, Types of filters, Stride, Padding, Pooling, Flatten, CNN Python project
- Recurrent Neural Network (RNN), RNN model, Operation of RNN model, Types; One-one RNN model, One-many RNN model, Many-many RNN model
Deep Learning Bootcamp with 5 Capstone Projects
Saturday, April 30, 2022
Deep Learning Bootcamp with 5 Capstone Projects
Learn about Deep Learning - ANN, CNN, RNN, LSTMs along with Real Time Capstone Projects
Preview this Course
What you'll learn
- Learn about Artificial Neural Networks.
- Learn about the different Layers present in a Neural Networks.
- Learn about different Activation Functions used in a Neural Network.
- Learn to Hyper tune the Neural Networks to Improve Performance.
- Implement Artificial Neural Networks to solve real world Problems.
- Learn about Convolutional Neural Networks.
- Learn about different Layers of a Convolutional Neural Networks.
- Learn about Dropout and Callbacks in Neural Networks.
- Learn about the Recurrent Neural Networks.
- Implement the LSTMs to solve Sequential Problems.
- Use Real World Examples
Requirements
- For taking up this course you need to be enthusiastic and self confident.
- You need to have good knowledge of programming and basic mathematical skills.
- Determination and Desire to Learn new things.
- No prior knowledge is needed.
- Start from the basics and gradually build your knowledge in the subject.
Description
Are you ready to master Deep Learning skills?
Deep Learning is a technology using which we can solve highly computational problems such as Image Processing, Image Classification, Image Segmentation, Image tagging, sound classification, video analysis, etc.
Deep Learning is becoming a buzzword these days, and If you want to learn Deep Learning then It is very important for you that you should have a proper plan regarding that.
Before Learning Deep Learning you must have learned Machine Learning and must possess good knowledge of the Python programming language.
If you want to build super-powerful applications in Deep Learning. Then, you are at the right place.
This course will provide you with in-depth knowledge on a very hot topic i.e., Deep Learning.
The purpose of this course is to provide you with knowledge of key aspects of Deep Learning without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets.
This course will cover the following topics:-
1. Deep Learning (DL).
2. Artificial Neural Network (ANN).
3. Convolutional Neural Network (CNN).
4. Recurrent Neural Network. (RCN)
5. Learn to Implement the LSTMs.
This course will take you through the basics to an advanced level in all the mentioned four topics.
After taking this course, you will be confident enough to work independently on any projects on these topics.
There are lots and lots of exercises for you to practice In this Deep Learning Course and also a 5 Bonus Deep Learning Project "Stock Market Prediction", "Fruits Identification System", "Face Expression Recognizer", "Detecting Pneumonia from Chest X-rays", and "Optimizing Crop Production".
In this Optimizing Crop Production, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity.
In this Detecting Pneumonia from X-rays project, you will learn how to solve Image Classification Tasks using Deep Neural Networks such as ResNet which is a High-Level CNN Architectures.
In this Stock Market Prediction project, you will learn to analyze, and the Stock Market Prices using Time Series Forecasting, Advanced Deep Learning Models, and different Statistical features.
In this Fruits Recognition project, you will learn how to solve a complicated Image Classification Task with Multiple Classes using various Deep Learning Architectures and Compare the Result.
In this Face Expression Recognizer project, you will learn to use Computer Vision Techniques to detect Human Emotions such as Angry, Sad, Happy, Disgust, Fear, etc. to build a Facial Emotion Detector.
Instructor Support - Quick Instructor Support for any queries.
I'm looking forward to see you in the course!
You will have access to all the resources used in this course.
Who this course is for:
- This course is for everyone who wants to master Deep Learning skills.
- This course is for students who have already good knowledge of machine learning and have good programming skills.
- Any people who want to create added value to their business by using powerful Deep Learning tools
- Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
- Software engineers who are curious about data science and about the Deep Learning buzz and want to get a better understanding of it
Computer Vision: Face Recognition Quick Starter in Python
Wednesday, March 30, 2022
Computer Vision: Face Recognition Quick Starter in Python - Quickly Build Python Deep Learning based Face Detection, Recognition, Emotion , Gender and Age Classification Systems
What you'll learn
- Face Detection from Images
- Face Detection from Realtime Videos
- Emotion Detection, Age-Gender Prediction
- Face Recognition from Images, Realtime Videos
Description
Hi There!
welcome to my new course 'Face Recognition with Deep Learning using Python'. This is the second course from my Computer Vision series.
Face Detection and Face Recognition is the most used applications of Computer Vision. Using these techniques, the computer will be able to extract one or more faces in an image or video and then compare it with the existing data to identify the people in that image.
Face Detection and Face Recognition is widely used by governments and organizations for surveillance and policing. We are also making use of it daily in many applications like face unlocking of cell phones etc.
This course will be a quick starter for people who wants to dive deep into face recognition using Python without having to deal with all the complexities and mathematics associated with typical Deep Learning process.
We will be using a python library called face-recognition which uses simple classes and methods to get the face recognition implemented with ease. We are also using OpenCV, Dlib and Pillow for python as supporting libraries.
Let's now see the list of interesting topics that are included in this course.
At first we will have an introductory theory session about Face Detection and Face Recognition technology.
After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package. Then we will install the rest of dependencies and libraries that we require including the dlib, face-recognition, opencv etc and will try a small program to see if everything is installed fine.
Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.
Then we will have an introduction to the basics and working of face detectors which will detect human faces from a given media. We will try the python code to detect the faces from a given image and will extract the faces as separate images.
Then we will go ahead with face detection from a video. We will be streaming the real-time live video from the computer's webcam and will try to detect faces from it. We will draw rectangle around each face detected in the live video.
In the next session, we will customize the face detection program to blur the detected faces dynamically from the webcam video stream.
After that we will try facial expression recognition using pre-trained deep learning model and will identify the facial emotions from the real-time webcam video as well as static images
And then we will try Age and Gender Prediction using pre-trained deep learning model and will identify the Age and Gender from the real-time webcam video as well as static images
After face detection, we will have an introduction to the basics and working of face recognition which will identify the faces already detected.
In the next session, We will try the python code to identify the names of people and their the faces from a given image and will draw a rectangle around the face with their names on it.
Then, like as we did in face detection we will go ahead with face recognition from a video. We will be streaming the real-time live video from the computer's webcam and will try to identify and name the faces in it. We will draw rectangle around each face detected and beneath that their names in the live video.
Most times during coding, along with the face matching decision, we may need to know how much matching the face is. For that we will get a parameter called face distance which is the magnitude of matching of two faces. We will later convert this face distance value to face matching percentage using simple mathematics.
In the coming two sessions, we will learn how to tweak the face landmark points used for face detection. We will draw line joining these face land mark points so that we can visualize the points in the face which the computer is used for evaluation.
Taking the landmark points customization to the next level, we will use the landmark points to create a custom face make-up for the face image.
That's all about the topics which are currently included in this quick course. The code, images and libraries used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.
Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.
So that's all for now, see you soon in the class room. Happy learning and have a great time.
Who this course is for:
- Beginners or who wants to start with Python based Face Recognition
Complete Guide to TensorFlow for Deep Learning with Python
Tuesday, March 15, 2022
Online Courses Udemy - Complete Guide to TensorFlow for Deep Learning with Python, Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques!
- BESTSELLER
- 4.5 (12,350 ratings)
- Created by Jose Portilla
- English [Auto-generated], French [Auto-generated], 9 more
PREVIEW THIS COURSE - GET COUPON CODE
What you'll learn
- Understand how Neural Networks Work
- Build your own Neural Network from Scratch with Python
- Use TensorFlow for Classification and Regression Tasks
- Use TensorFlow for Image Classification with Convolutional Neural Networks
- Use TensorFlow for Time Series Analysis with Recurrent Neural Networks
- Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
- Learn how to conduct Reinforcement Learning with OpenAI Gym
- Create Generative Adversarial Networks with TensorFlow
- Become a Deep Learning Guru!
Requirements
- Some knowledge of programming (preferably Python)
- Some basic knowledge of math (mean, standard deviation, etc..)
Description
Welcome to the Complete Guide to TensorFlow for Deep Learning with Python!
This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!
This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics, including
- Neural Network Basics
- TensorFlow Basics
- Artificial Neural Networks
- Densely Connected Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- AutoEncoders
- Reinforcement Learning
- OpenAI Gym
- and much more!
There are many Deep Learning Frameworks out there, so why use TensorFlow?
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!
Become a machine learning guru today! We'll see you inside the course!
Who this course is for:
- Python students eager to learn the latest Deep Learning Techniques with TensorFlow
Practical AI with Python and Reinforcement Learning
Practical AI with Python and Reinforcement Learning - Learn how to use Reinforcement Learning techniques to create practical Artificial Intelligence programs!
- Hot & New
- Created by Jose Portilla
- English [Auto]
What you'll learn
- Reinforcement Learning with Python
- Creating Artificial Neural Networks with TensorFlow
- Using TensorFlow to create Convolution Neural Networks for Images
- Using OpenAI to work with built-in game environments
- Using OpenAI to create your own environments for any problem
- Create Artificially Intelligent Agents
- Tabular Q-Learning
- State–action–reward–state–action (SARSA)
- Deep Q-Learning (DQN)
- DQN using Convolutional Neural Networks
- Cross Entropy Method for Reinforcement Learning
- Double DQN
- Dueling DQN
Description
Please note! This course is in an "early bird" release, and we're still updating and adding content to it, please keep in mind before enrolling that the course is not yet complete.
“The future is already here – it’s just not very evenly distributed.“
Have you ever wondered how Artificial Intelligence actually works? Do you want to be able to harness the power of neural networks and reinforcement learning to create intelligent agents that can solve tasks with human level complexity?
This is the ultimate course online for learning how to use Python to harness the power of Neural Networks to create Artificially Intelligent agents!
This course focuses on a practical approach that puts you in the driver's seat to actually build and create intelligent agents, instead of just showing you small toy examples like many other online courses. Here we focus on giving you the power to apply artificial intelligence to your own problems, environments, and situations, not just those included in a niche library!
This course covers the following topics:
Artificial Neural Networks
Convolution Neural Networks
Classical Q-Learning
Deep Q-Learning
SARSA
Cross Entropy Methods
Double DQN
and much more!
We've designed this course to get you to be able to create your own deep reinforcement learning agents on your own environments. It focuses on a practical approach with the right balance of theory and intuition with useable code. The course uses clear examples in slides to connect mathematical equations to practical code implementation, before showing how to manually implement the equations that conduct reinforcement learning.
We'll first show you how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as Q-Learning. Then we can combine these ideas to walk you through Deep Reinforcement Learning agents, such as Deep Q-Networks!
There is still a lot more to come, I hope you'll join us inside the course!
Jose
Who this course is for:
- Python developers familiar with basics of machine learning, such as Scikit-Learn, but now want to learn how to create Artificially Intelligent Agents through Reinforcement Learning
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
Computer Vision: YOLO Custom Object Detection with Colab GPU
Tuesday, March 8, 2022
Computer Vision: YOLO Custom Object Detection with Colab GPU - YOLO: Pre-Trained Coco Dataset and Custom Trained Coronavirus Object Detection Model with Google Colab GPU Training
What you'll learn
- Python based YOLO Object Detection using Pre-trained Dataset Models as well as Custom Trained Dataset Models. Case study of coronavirus detector using YOLO
- YOLO Custom Training
- YOLO V4 Object Detection
- YOLO V4 Object Recognition
Description
Hi There!
welcome to my new course 'YOLO Custom Object Detection Quick Starter with Python'. This is the fourth course from my Computer Vision series.
As you know Object Detection is the most used applications of Computer Vision, in which the computer will be able to recognize and classify objects inside an image.
We will be specifically focusing on (YOLO), You only look once which is an effective real-time object recognition algorithm which is featured in Darknet, an open source neural network framework
This course is equally divided into two halves. The first half will deal with object recognition using a predefined dataset called the coco dataset which can classify 80 classes of objects. And the second half we will try to create our own custom dataset and train the YOLO model. We will try to create our own coronavirus detection model.
Let's now see the list of interesting topics that are included in this course.
At first we will have an introductory theory session about YOLO Object Detection system.
After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine.
Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.
Then we will install install OpenCV, which is the Open Source Computer Vision library in Python.
Then we will have an introduction to Convolutional Neural Networks , its working and the different steps involved.
Now we will proceed with the part 1 that involves Object Detection and Recognition using YOLO pre-trained model. we will have an overview about the yolo model in the next session and then we will implement yolo object detection from a single image.
Often YOLO gives back more than one successful detection for a single object in an image. This can be fixed using
a technique called as NMS or Non Maxima Suppression. We will implement that in our next session.
And using that as the base, we will try the yolo model for object detection from a real time webcam video and we will check the performance. Later we will use it for object recognition from the pre-saved video file.
Then we will proceed with part 2 of the course in which we will attempt to train a darknet YOLO model. A model which can detect coronavirus from an electron microscope image or video output.
Before we proceed with the implementation, we will discuss the pros and cons of using a pre-trained dataset model and a custom dataset trained model. Also about the free GPU offered by google colab and its features.
In the next session we will start with phase 1 of our custom model in which we will do the preparation steps to implement custom model. We will at first download the darknet source from github and prepare it. We will then download the weight files required for both testing and training. And then we will edit the required configurations files to make it ready for our custom coronavirus detector.
In the second phase for our custom model, we will start collecting the required data to train the model. We will collect coronavirus images from the internet as much as we could and organize them into folder. Then we will label or annotate the coronavirus object inside these images using an opensource annotation tool called labelImg. Then we will split the gathered dataset, 80% for training and 20% for testing. And finally will edit the prepare the files with the location of training and testing datasets.
Now that we have all our files ready, in our third phase, we will zip and upload them into google drive. After that we will create a google colab notebook and configure the colab runtime to use the fast, powerful, yet free GPU service provided by google. Then we will mount our google drive to our colab runtime and unzip the darknet zip we uploaded.
Sometimes files edited in non unix environments may be having problems when compiling the darknet. We have to convert the encoding from dos to unix as our next step. Then we will complile the darknet framework source code and proceed with testing the darknet framework with a sample image in our fourth phase.
The free GPU based runtime provided by google colab is volatile. It will get reset every 12 hours. So we need to save our weights periodically during training to our google drive which is a permanent storage. So in our phase five, we will link a backup folder in google drive to the colab runtime.
Finally in our phase 6, we are ready to proceed with training our custom coronavirus model. We will keep on monitoring the loss for every iteration or epoch as we call it in nerual network terms. Our model will automatically save the weights every 100th epoch securely to our google drive backup folder.
We can see a continues decrease in the loss values as we go through the epoch. And after many number of iterations, our model will come into a convergence or flatline state in which there is no further improvement in loss. at that time we will obtain a final weight
Later we will use that weight to do prediction for an image that contains coronavirus in it. We can see that our model clearly detects objects. We will even try this with a video file also.
We cannot claim that its a fully fledged flawless production ready coronavirus detection model. There is still room for improvement. But anyway, by building this custom model, we came all the way through the steps and process of making a custom yolo model which will be a great and valuable experience for you.
And then later in a quick session, we will also discuss few other case studies in which we can implement a custom trained YOLO model, the changes we may need to make for training those models etc.
That's all about the topics which are currently included in this quick course. The code, images and weights used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.
Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.
So that's all for now, see you soon in the class room. Happy learning and have a great time.
Who this course is for:
- Beginners or who those wants to start with Python based Object Recognition and want to develop custom object detection models
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