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

Deep Learning A-Z™: Hands-On Artificial Neural Networks Course Catalog Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.
Deep Learning A-Z™: Hands-On Artificial Neural Networks Course Catalog Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

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

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

What you’ll learn

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

  • 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

  • High school mathematics level
  • Basic Python programming knowledge

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.

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

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

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 that 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. 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. – Course Catalog

No matter how complex your query, we will be there. The bottom line is we want you to succeed.

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

— The Tools —

Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course, you will learn both!

Companies using Tensorflow include Airbnb, Airbus, eBay, Intel, Uber and dozens more.

Companies using PyTorch include Twitter, Salesforce, 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
  • learn how 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. Course Catalog

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? —

If you are just starting out into Deep Learning, then you will find this course extremely useful. 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. 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). To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach. Deep Learning A-Z™: Hands-On Artificial Neural Networks Course Catalog

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.

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 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 a 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 Catalog!

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.

 #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.

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.

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.

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!

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
  • Any students in college who want to start a career in Data Science
  • Anyone data analysts who want to level up in Deep Learning
  • Any people who want to create added value to their business by using powerful Deep Learning tools
  • Anyone 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
  • Content From: https://www.udemy.com/course/deeplearning/
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  • Last updated 2/2020

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

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