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Deep Learning A-Z™: Hands-On Artificial Neural Networks

With two Machine Learning and Data Science experts, you’ll learn how to create Deep Learning Algorithms in Python. Templates included.

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

Requirements

Description

*** As seen on Kickstarter ***

Certainly, artificial intelligence is growing exponentially. Self-driving cars are running millions of miles, IBM Watson is diagnosing patients better than armies of doctors, and AlphaGo beat the World champion at Go, a game where intuition plays a key role.

But the further AI advances, the more complex the problems it has to solve. And only Deep Learning can solve such complex problems, and that’s why it forms the basis of Artificial intelligence.

— Why Deep Learning A-Z? —

Our five reasons on why Deep Learning A-Z is truly different and stands out from dozens of other training programs are:

1. ROBUST STRUCTURE 

Deep Learning is a very broad and complex field, so the first and most important thing to focus on is providing the Deep Learning A-Z™: Hands-On Artificial Neural Networks course a robust structure. To navigate this maze, one must have a clear global perspective.

Because of this, we have grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. By concentrating on three distinct algorithms per volume, we found this to be the most efficient structure for learning Deep Learning.

2. INTUITION TUTORIALS

Lots of Deep Learning A-Z™: Hands-On Artificial Neural Networks courses and books bombard you with theory, math, and coding… But they fail to explain the most important part: why you are doing what you’re doing.
That’s why this Deep Learning A-Z™: Hands-On Artificial Neural Networks course is so different. We emphasize developing an intuitive feel for the concepts behind Deep Learning algorithms.

With our intuition tutorials you will be confident that you will understand each technique instinctively. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be.

3. EXCITING PROJECTS

Are you tired of courses based on over-used, outdated data sets?

Yes? Well then you’re in for a treat.

Our goal in this class is to solve Real-World business problems using Real-World datasets. (Not the boring iris and digits datasets we see in every class) We will tackle six real-world problems during the Deep Learning A-Z™: Hands-On Artificial Neural Networks course:

  • 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. 

Moreover, we will structure the source code so that you can download it and directly manipulate it for your own projects. Moreover, we show you step-by-step how you can modify the code to incorporate YOUR dataset, to tweak the algorithm to your needs, to evaluate the data you have.

This is a Deep Learning A-Z™: Hands-On Artificial Neural Networks course which naturally extends into your career.

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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 Deep Learning A-Z™: Hands-On Artificial Neural Networks 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.

Because we are also physically capable of eating and sleeping, we have assembled a team of professional Data Scientists in our office to assist us. You will receive a response from us within 48 hours at the most.

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 Deep Learning A-Z™: Hands-On Artificial Neural Networks 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, this Deep Learning A-Z™: Hands-On Artificial Neural Networks course will give you the chance to understand the benefits and shortcomings of both Tensorflow and PyTorch. Throughout the tutorials, we compare them both and provide you with tips and ideas as to which might work best in certain contexts.

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 Deep Learning A-Z™: Hands-On Artificial Neural Networks 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.

It is a library that implements Deep Learning models and they act like a wrapper for Theano and Tensorflow. Keras provides the tools to implement powerful Deep Learning models with only a few lines of code. It is this library that will allow you to see your overall vision of what you are creating in great clarity. Everything you do will have a clear structure thanks to this library, and you will really get the intuition into 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 Deep Learning A-Z™: Hands-On Artificial Neural Networks 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 many tools in the Deep Learning space, and in this Deep Learning A-Z™: Hands-On Artificial Neural Networks course, we will show you the most important ones and the most progressive ones so that when you finish Deep Learning A-ZTM your abilities are on the cutting edge of today’s technology.

If you’re just beginning your journey into Deep Learning, then this course will prove beneficial to you. 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 Deep Learning A-Z™: Hands-On Artificial Neural Networks course. The tutorials you attend will help you acquire more knowledge and you will start feeling increasingly confident with each one.

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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 —

It’s not just about knowing the framework and tools, it’s about being able to apply these models to real-world scenarios and derive actual measurable results for the project or business. Hence, the six exciting challenges we are introducing in this course:

#1 Churn Modelling Problem

This part of the Deep Learning A-Z™: Hands-On Artificial Neural Networks course will have you solving a data analytics question for a bank. You will be provided with a dataset that includes a sample of the bank’s clients. The bank compiled customer data such as their names, email addresses, telephone numbers, credit scores, genders, age, tenure, balance, and whether or not they have a credit card during the last year. For six months, the bank observed if these customers left the bank or stayed.

Based on geo-demographic and transactional data provided above, your objective is to make an Artificial Neural Network that can predict which customers will leave the bank or remain (customer churn). Moreover, you are asked to rank all the bank’s customers according to their probability of leaving the bank. To do that, you will need the right Deep Learning model, one that takes 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

The objective of this part is to create a Convolutional Neural Network capable of detecting various objects in images. We will use this Deep Learning model to recognize a cat or a dog in a set of images. This model can be reused for anything else and we will show you exactly how to do it by simply changing the images 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. 

A Recurrent Neural Network is a branch of Deep Learning which facilitates this. Classic RNN are very weak and have a short memory, for that reason they did not gain popularity or become powerful. 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. Our students are extremely excited about the fact that these cutting-edge methods are included 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 Deep Learning A-Z™: Hands-On Artificial Neural Networks course.  

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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”. 

Final Recommender System can also predict customer ratings for movies not watched by the customers. The Deep Learning model will provide recommendations based on the ranking from 5 down to 1 on which movies each user should watch. Making such a powerful Recommender System is quite the challenge, so we will try 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.

The lessons inside of them can be applied to yourself or your friends. There will be a list of movies that you must rate, so you only need to rate the movies you actually watched and input your ratings into the dataset. Afterward, you are ready to run your model. If you are bored watching Netflix on a given night and don’t have ideas what to watch, you can use the Recommender System to figure out what films you’ll enjoy!

— 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

Created by Kirill Eremenko, Hadelin de Ponteves, Ligency Team
Last updated 4/2021
English
Size: 4.76 GB

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https://www.udemy.com/course/deeplearning/

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