Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize from Udemy

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. 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... 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.

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-

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

What's inside

Learning objectives

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
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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
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Show less

Syllabus

Welcome to the course!

Welcome Challenge!

Get to know what is Deep Learning, how it developed over the years and how it will affect the future

Get the Datasets here

EXTRA: Use ChatGPT to Boost your Deep Learning Skills

--------------------- Part 1 - Artificial Neural Networks ---------------------

Welcome to Part 1 - Artificial Neural Networks

ANN Intuition

What You'll Need for ANN

Plan of Attack

The Neuron

The Activation Function

Learn step-by-step how do Neural Networks work, focused on actual applications and close to life example.

How do Neural Networks learn?

Gradient Descent

Stochastic Gradient Descent

Backpropagation

Building an ANN

Business Problem Description

IMPORTANT NOTE

Building an ANN - Step 1

Check out our free course on ANN for Regression

Building an ANN - Step 2

Building an ANN - Step 3

Building an ANN - Step 4

Building an ANN - Step 5

-------------------- Part 2 - Convolutional Neural Networks --------------------

Welcome to Part 2 - Convolutional Neural Networks

CNN Intuition

What You'll Need for CNN

Plan of attack

What are convolutional neural networks?

Step 1 - Convolution Operation

Step 1(b) - ReLU Layer

Step 2 - Pooling

Step 3 - Flattening

Step 4 - Full Connection

Summary

Softmax & Cross-Entropy

Building a CNN

Building a CNN - Step 1

Building a CNN - Step 2

Building a CNN - Step 3

Building a CNN - Step 4

Building a CNN - Step 5

Quick Note

Building a CNN - FINAL DEMO!

---------------------- Part 3 - Recurrent Neural Networks ----------------------

Welcome to Part 3 - Recurrent Neural Networks

RNN Intuition

What You'll Need for RNN

The idea behind Recurrent Neural Networks

The Vanishing Gradient Problem

LSTMs

Practical intuition

EXTRA: LSTM Variations

Building a RNN

Building a RNN - Step 1

Building a RNN - Step 2

Building a RNN - Step 3

Building a RNN - Step 4

Building a RNN - Step 5

Building a RNN - Step 6

Building a RNN - Step 7

Building a RNN - Step 8

Building a RNN - Step 9

Building a RNN - Step 10

Building a RNN - Step 11

Building a RNN - Step 12

Building a RNN - Step 13

Building a RNN - Step 14

Building a RNN - Step 15

Evaluating and Improving the RNN

Evaluating the RNN

Improving the RNN

------------------------ Part 4 - Self Organizing Maps ------------------------

Welcome to Part 4 - Self Organizing Maps

SOMs Intuition

How do Self-Organizing Maps Work?

Why revisit K-Means?

K-Means Clustering (Refresher)

How do Self-Organizing Maps Learn? (Part 1)

How do Self-Organizing Maps Learn? (Part 2)

Live SOM example

Reading an Advanced SOM

EXTRA: K-means Clustering (part 2)

EXTRA: K-means Clustering (part 3)

Building a SOM

How to get the dataset

Building a SOM - Step 1

Building a SOM - Step 2

Building a SOM - Step 3

Building a SOM - Step 4

Mega Case Study

Mega Case Study - Step 1

Mega Case Study - Step 2

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops advanced professional skills in Deep Learning

Covers the latest cutting-edge techniques and algorithms for Deep Learning

Provides hands-on experience with real-world business problems

Suitable for beginners with no prior knowledge of Deep Learning

Also suitable for intermediate learners looking to enhance their Deep Learning skills

Reviews summary

In-depth deep learning coverage

Learners say this course offers an in-depth coverage of deep learning, neural networks, and PyTorch. While some find the material comprehensive, others have difficulty understanding the use of PyTorch. Overall, responses are largely positive.

Learners appreciate the thoroughness of the course.

"A good course that covered most of the stuff in deep learning, neural network."

Some students struggle with the implementation of PyTorch.

"do have a hard time understanding the PyTorch used in Volume 2 - Unsupervised Deep Learning."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize with these activities:

Attend a Deep Learning Meetup

Show steps

Network with other people who are interested in Deep Learning. This will help you learn about new trends in Deep Learning and find potential collaborators.

Browse courses on Deep Learning

Show steps

Find a Deep Learning meetup in your area.
Attend the meetup.
Introduce yourself to other people and talk about your interests in Deep Learning.

Follow a Deep Learning Tutorial Series

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Learn about Deep Learning by following a tutorial series. This will help you develop a strong foundation in the fundamentals of Deep Learning.

Browse courses on Deep Learning

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Find a Deep Learning tutorial series that is appropriate for your skill level.
Follow the tutorials in the series.
Complete the exercises in the series.

Volunteer for a Deep Learning Project

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Contribute to the Deep Learning community by volunteering for a project. This will help you develop your skills and make a positive impact.

Browse courses on Deep Learning

Show steps

Find a Deep Learning project that you are interested in.
Contact the project leader and express your interest in volunteering.
Complete the tasks that are assigned to you.

Six other activities

Expand to see all activities and additional details

Show all nine activities

Review Deep Learning

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Familiarize yourself with some of the core concepts of Deep Learning as well as how Deep Learning has developed over the years.

View Deep Learning on Amazon

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Read Chapter 1 of the book Deep Learning.
Watch the lecture on the history of Deep Learning.
Complete the quiz on Chapter 1.

Attend a Deep Learning Workshop

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Learn about Deep Learning from experts in the field. This will help you develop a deeper understanding of Deep Learning and its applications.

Browse courses on Deep Learning

Show steps

Find a Deep Learning workshop that is appropriate for your skill level.
Register for the workshop.
Attend the workshop.

Solve Deep Learning Coding Challenges

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Improve your Deep Learning coding skills by solving coding challenges. This will help you become more proficient in implementing Deep Learning models.

Browse courses on Deep Learning

Show steps

Find a website or platform that offers Deep Learning coding challenges.
Choose a challenge and try to solve it.
If you get stuck, refer to the documentation or ask for help on a forum.

Build a Simple Neural Network

Show steps

Practice building and training a simple neural network in Python. This will help you develop a deeper understanding of how neural networks work.

Browse courses on Neural Networks

Show steps

Install the necessary libraries.
Create a dataset.
Build a neural network model.
Train the model.
Evaluate the model.

Write a Blog Post on Deep Learning

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Deepen your understanding of Deep Learning by explaining it to others. This will help you solidify your knowledge and identify areas where you need further improvement.

Browse courses on Deep Learning

Show steps

Choose a topic for your blog post.
Research the topic.
Write the blog post.
Edit and proofread your blog post.
Publish your blog post.

Develop a Deep Learning Model for a Real-World Problem

Show steps

Apply your Deep Learning skills to solve a real-world problem. This will help you develop a deeper understanding of how Deep Learning can be used to make a positive impact.

Browse courses on Deep Learning

Show steps

Identify a real-world problem that you would like to solve.
Collect data.
Build a Deep Learning model.
Train the model.
Evaluate the model.
Deploy the model.

Career center

Learners who complete Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists gather and analyze large datasets, using their findings to improve business operations. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in data science to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models to solve complex business problems. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in machine learning to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Engineer

Artificial Intelligence Engineers design and develop artificial intelligence systems to solve complex business problems. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in artificial intelligence to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as an Artificial Intelligence Engineer.

See salaries and explore the career path for Artificial Intelligence Engineer

Data Analyst

Data Analysts gather and analyze data to identify trends and patterns that can help businesses make better decisions. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in data analysis to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Data Analyst.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in software engineering to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Software Engineer.

See salaries and explore the career path for Software Engineer

Financial Analyst

Financial Analysts use financial data to make investment decisions. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in financial analysis to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Data Engineer

Data Engineers design and build data pipelines to collect, store, and process data. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in data engineering to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Data Engineer.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in quantitative analysis to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in actuarial science to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as an Actuary.

See salaries and explore the career path for Actuary

Computer Scientist

Computer Scientists design and develop computer systems and software. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in computer science to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Computer Scientist.

See salaries and explore the career path for Computer Scientist

Software Developer

Software Developers design, develop, and maintain software applications. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in software development to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Software Developer.

See salaries and explore the career path for Software Developer

Market Researcher

Market Researchers collect and analyze data about consumers and markets. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in market research to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Market Researcher.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency of business operations. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in operations research to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

Business Analysts use data to identify opportunities and improve business performance. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in business analysis to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Business Analyst.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians use statistical methods to analyze data and draw conclusions. The Deep Learning A-Z course teaches the fundamentals of deep learning, a powerful technique used in statistics to solve complex problems. The course also includes real-world case studies, so learners can apply their knowledge to practical business challenges. This course can help you build a strong foundation in deep learning, which is essential for success as a Statistician.

See salaries and explore the career path for Statistician