Showing posts with label Neural Networks. Show all posts
Showing posts with label Neural Networks. Show all posts
Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize
Friday, November 15, 2024
Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize,Learn to create Deep Learning models in Python from two Machine Learning, Data Science experts. Code templates included.
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"Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize" sounds like an exciting event or competition focused on advancing knowledge and applications in deep learning, artificial intelligence, and conversational AI, potentially involving the use of ChatGPT. Here's a breakdown of what such an event might entail:
1. **Deep Learning A-Z 2024**:
- This could be a conference, workshop, or online course focused on deep learning, covering topics ranging from foundational concepts to advanced techniques and applications.
2. **Neural Networks**:
- Sessions or modules dedicated to neural networks, including basic architectures like feedforward neural networks to advanced models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
3. **AI & ChatGPT**:
- Special sessions or workshops focused on artificial intelligence (AI) and conversational AI, with a particular emphasis on models like ChatGPT for natural language understanding and generation.
4. **Prize**:
- The "Prize" aspect suggests that there might be a competition or incentive program associated with the event. Participants could compete for prizes by demonstrating their skills and creativity in deep learning, AI, or ChatGPT-related tasks.
- Prizes might include cash rewards, access to advanced training resources, opportunities for collaboration or mentorship with industry experts, or recognition within the AI community.
5. **Format**:
- The event could be structured as a series of lectures, hands-on workshops, coding challenges, hackathons, or research paper presentations.
- Participants may have the opportunity to showcase their projects, research findings, or innovative applications of deep learning and AI, including projects involving ChatGPT.
6. **Goals**:
- The overarching goal of the event would likely be to foster learning, collaboration, and innovation in the fields of deep learning and AI, while also promoting the development and application of conversational AI technologies like ChatGPT.
Overall, "Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize" could serve as a platform for researchers, practitioners, and enthusiasts to exchange ideas, learn from experts, and push the boundaries of what's possible with deep learning and AI, particularly in the context of conversational interfaces powered by models like ChatGPT.
Artificial Neural Networks for Business Managers in R Studio
Tuesday, June 8, 2021
Artificial Neural Networks for Business Managers in R Studio - You do not need coding or advanced mathematics background for this course. Understand how predictive ANN models work
- Created by Start-Tech Academy
- English [Auto]
What you'll learn
- Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
- Understand the business scenarios where Artificial Neural Networks (ANN) is applicable
- Building a Artificial Neural Networks (ANN) in R
- Use Artificial Neural Networks (ANN) to make predictions
- Use R programming language to manipulate data and make statistical computations
- Learn usage of Keras and Tensorflow libraries
Description
You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right?
You've found the right Neural Networks course!
After completing this course you will be able to:
Identify the business problem which can be solved using Neural network Models.
Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.
Create Neural network models in R using Keras and Tensorflow libraries and analyze their results.
Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in R Studio without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman - Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:
Part 1 - Setting up R studio and R Crash course
This part gets you started with R.
This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.
Part 2 - Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
Part 3 - Creating Regression and Classification ANN model in R
In this part you will learn how to create ANN models in R Studio.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.
Part 4 - Data Preprocessing
In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.
In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split.
Part 5 - Classic ML technique - Linear Regression
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don't understand
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a Neural Network model in R will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
Start-Tech Academy
------------
Below are some popular FAQs of students who want to start their Deep learning journey-
Why use R for Deep Learning?
Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R
1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.
2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.
4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Neural Network journey
Statisticians needing more practical experience
Anyone curious to master ANN from Beginner level in short span of time
Neural Networks in Python: Deep Learning for Beginners
Thursday, March 4, 2021
Neural Networks in Python: Deep Learning for Beginners, Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python
- Created by Start-Tech Academy
- English [Auto]
Description
You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right?
You've found the right Neural Networks course!
After completing this course you will be able to:
Identify the business problem which can be solved using Neural network Models.
Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.
Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results.
Confidently practice, discuss and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman - Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:
Part 1 - Python basics
This part gets you started with Python.
This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
Part 2 - Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
Part 3 - Creating Regression and Classification ANN model in Python
In this part you will learn how to create ANN models in Python.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.
Part 4 - Data Preprocessing
In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.
In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split.
Part 5 - Classic ML technique - Linear Regression
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don't understand
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Cheers
Start-Tech Academy
------------
Below are some popular FAQs of students who want to start their Deep learning journey-
Why use Python for Deep Learning?
Understanding Python is one of the valuable skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Neural Network journey
Statisticians needing more practical experience
Anyone curious to master ANN from Beginner level in short span of time
Neural Networks in Python from Scratch: Complete guide
Thursday, February 25, 2021
Free Coupon Discount - Neural Networks in Python from Scratch: Complete guide, Learn the fundamentals of Deep Learning of neural networks in Python both in theory and practice! | Created by Jones Granatyr, Kirill Eremenko, 2 others
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Description
Artificial neural networks are considered to be the most efficient Machine Learning techniques nowadays, with companies the likes of Google, IBM and Microsoft applying them in a myriad of ways. You’ve probably heard about self-driving cars or applications that create new songs, poems, images and even entire movie scripts! The interesting thing about this is that most of these were built using neural networks. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis.
One of the biggest problems that I’ve seen in students that start learning about neural networks is the lack of easily understandable content. This is due to the fact that the majority of the materials that are available are very technical and apply a lot of mathematical formulas, which simply makes the learning process incredibly difficult for whomever wishes to take their first steps in this field. With this in mind, the main objective of this course is to present the theoretical and mathematical concepts of neural networks in a simple yet thorough way, so even if you know nothing about neural networks, you’ll understand all the processes. We’ll cover concepts such as perceptrons, activation functions, multilayer networks, gradient descent and backpropagation algorithms, which form the foundations through which you will understand fully how a neural network is made. We’ll also cover the implementations on a step-by-step basis using Python, which is one of the most popular programming languages in the field of Data Science. It’s important to highlight that the step-by-step implementations will be done without using Machine Learning-specific Python libraries, because the idea behind this course is for you to understand how to do all the calculations necessary in order to build a neural network from scratch.
To sum it all up, if you wish to take your first steps in Deep Learning, this course will give you everything you need. It’s also important to note that this course is for students who are getting started with neural networks, therefore the explanations will deliberately be slow and cover each step thoroughly in order for you to learn the content in the best way possible. On the other hand, if you already know your way around neural networks, this course will be very useful for you to revise and review some important concepts.
Are you ready to take the next step in your professional career? I’ll see you in the course!
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The Visual Guide on How Neural Networks Learn from Data
Saturday, August 29, 2020
Free Coupon Discount - The Visual Guide on How Neural Networks Learn from Data, The BEST Resource for Understanding Neural Networks and How They Learn
Created by Mauricio Maroto
Preview this Udemy Course GET COUPON CODE
Description
Big announcemen: This course has gone FREE!
Enroll now!
Course Achievements (October 2019):
+2,000 Worldwide Students enrolled
Trophy Awards for Key Section Achievements!
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Some Student Reviews are:
"Very structured and logical" (July 2018).
"Great Explanation of NN. Will definitely recommend to others." (April 2018)
"Enlightening overview of how neural networks operate mathematically." (March 2018).
"An Excellent Course" (February 2018).
"The NN is a Complex topic and the instructor explains the NN very clearly. The demonstration shows how NN learns from data in a very precise way. I highly recommend this course." (January 2018).
"I just loved this course. The course is very well taught and is divided in easy-to-digest units." (December 2017).
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More Student Reviews:
"Highly visual. Good explaining. Easy to grasp. I can't praise this work enough! Absolutely brilliant teaching material" (November 2017)
"excellently delivered step by step .. visually learning is very clear and easily understandable." (November 2017)
"Very clear and straight forward. Course is a great first step to understand the structure of a NN, and how it works." (November 2017)
"The course is interesting because it explains the basic mathematics of a neural network in a very simple and intuitive way." (October 2017)
"Very clear, very interesting and keeps u motivated to learn more." (September 2017)
"This is the best example and explanation I could find about the internal working of NN (...)" (August 2017)
"This is a unique way of explaining and illustrating the operation of a simple ANN." (July 2017)
"Excellent course! It teaches you the basic of Neural Network in an easy to understand way (...)" (June 2017)
"Great starting point to learn ANNs!" (June 2017)
"v[ery] good explaination" (May 2017)
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Hi. Thanks for showing interest in this course!
What makes this course special:
Step-by-Step Neural Network Learning Process,
Master topics like Fundamentals, Objectives, Required Datasets, Weights, Biases, Nodes, Activation functions, Feed-Forward Passes, Predictions, Losses, Gradient Descent, Learning, Backpropagation and more!
Plus, personalized feedback and help. You ask, I answer directly!
This is your BEST resource for Neural Networks (NN) learning! A must for understanding special concepts and not get lost in computing your own NNs:
✅ First:
You'll start the Neural Networks Primer with Fundamentals, Objectives, Data and more:
Learn concepts using analogies for maximum learning, so you will be fully covered.
Learning how NNs learn will be easy with this Primer under your sleeve!
✅ Second:
You'll continue the NN Primer with Learning, Backpropagation and Predictions and more topics
In an easy and intuitive way, you will understand how they work,
This is fundamental in the NN Learning Process.
At the end of this section, you will have mastered the NN Primer!
Now, you are ready for the Step-by-Step (in-Motion) sections!
✅ Third:
You'll start the in-Motion section with Inputs, Weights, Biases, Activations, Nodes and Feed-Forward Passes:
See how they work inside an NN,
Step-by-step templates, so you can follow every detail,
These files will be dynamic, so you'll understand how NNs work as numbers will be updated on-the-fly and right in front of your eyes.
✅ Forth:
You'll continue with the in-Motion section with NN Learning, Backpropagation, Tuning and Prediction:
You will understand how NNs learn from the data.
This all part of the dynamic templates you get to keep.
You'll do several examples along the way for maximum learning.
Lastly, you'll see what NNs do to make the best predictions.
✅ Fifth:
You'll finish the in-Motion section by doing a complete rundown on everyting you've learned so far:
You'll see how all NN inner components work for learning and prediction.
Pay close attention at how all parts adjust, making the NN learn in front of your eyes.
After this section, you will be fully versed on how NNs learn!
✅ Sixth:
I will devote a section for more additional knowledge and resources for continous learning. And then, I will conclude with some Final Words.
What are the Requirements?
The only thing you'll need for this course is: Excel and PowerPoint: It is that easy!
You will also need to bring your Basic Maths too,
If you bring your Calculus (Derivatives) knowledge, that will be a big plus for you (but not required),
What are some of the Benefits?
As it is usual in my courses, you will get all files and spreadsheets for all lectures.
This way you can replicate everything I do immediately after each lecture.
Neural Networks are the new thing today.
With it, you can explore and engage Artificial Intelligence, which I recommend you to dive in as it's part of the future.
Plus, it's very rewarding and fun too!
New content coming in the near future, let me know yout thoughts.
Lastly, you can post questions or doubts, and I’ll answer to you personally.
I hope you find this course as useful as I have creating it!
I’ll see you inside,
-M.A. Mauricio M.
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