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

Deep Learning A-Z™ with Python is an extensive guide to implementing deep learning models for text, image, sound, and beyond. Written by Jason Brownlee—who has trained deep neural networks on over $100 million in venture capital as a data scientist at multiple Silicon Valley startups, and Matthieu Guillaume—a software engineer from Nuxeo , this course has been thoroughly reviewed and approved as part of your artificial intelligence curriculum.

What you’ll learn in Deep Learning A-Z™: Hands-On Artificial Neural Networks

  1. Learn how Artificial Neural Networks work and how to use them.
  2. Incorporate Artificial Neural Networks into your day-to-day operations.
  3. Convolutional Neural Networks (CNNs): Understand the Intuition
  4. Practice with Convolutional Neural Networks
  5. Recurrent Neural Networks: Understanding the Intuition
  6. Practice with Recurrent Neural Networks
  7. Self-Organizing Maps: Understand the Intuition
  8. Self-Organizing Maps can be used in the real world.
  9. Boltzmann Machines: What They Are and How They Work
  10. In practice, use Boltzmann Machines.
  11. Learn how AutoEncoders work and why they’re useful.
  12. In practice, use AutoEncoders.


  • mathematics in high school
  • Programming basics in Python


*** This project was funded on Kickstarter ***
There’s no denying that artificial intelligence is advancing at a breakneck pace. Self-driving cars have logged millions of miles, IBM Watson is diagnosing patients better than armies of doctors, and Google Deepmind’s AlphaGo defeated the World champion at Go, a game in which intuition is crucial.

— What is the A-Z of Deep Learning?
Here are five reasons why we believe Deep Learning A-ZTM is unique among training programs:
The first and most important thing we focused on was giving the course a solid structure; Deep Learning is a vast and complex field, and navigating it requires a broad and comprehensive understanding of it.

So many courses and books focus on the theory, math, and coding… but they overlook the most important part: why you’re doing what you’re doing. That’s why this course is so unique: we focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms.

Are you sick of taking courses that are based on old, out-of-date data?
If so, you’re in for a real treat.
We will solve six real-world challenges in this class, using Real-World datasets to solve Real-World business problems (rather than the usual iris or digit classification datasets).

Every practical tutorial in Deep Learning A-ZTM begins with a blank page and we write the code from scratch, so you can follow along and understand exactly how the code works and what each line means.
Furthermore, we will purposefully structure the code in such a way that you can download it and use it in your own projects, and we will explain step-by-step where and how to modify the code to insert YOUR dataset, tailor the algorithm to your needs, and obtain the desired output.

Have you ever had a question about a course or a book but couldn’t get in touch with the author?
This course, on the other hand, is unique in that we are fully committed to making it the most disruptive and powerful Deep Learning course on the planet, which means we must always be available to assist you.

We will be there for you, no matter how complicated your question is; ultimately, we want you to succeed.
The Equipment —
The two most popular open-source Deep Learning libraries are Tensorflow and Pytorch, and you’ll learn both in this course!

Twitter, Saleforce, and Facebook are among the companies that use PyTorch, which is being developed by researchers at Nvidia and leading universities such as Stanford, Oxford, and ParisTech.
So, which is the better option, and why?
In this course, you’ll get a chance to work with both and learn when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials, we’ll compare the two and give you tips and ideas on which might work best in different situations.

— Additional Equipment —
Although Theano is very similar to Tensorflow in terms of functionality, we will still cover it.
Keras is an incredible library for implementing Deep Learning models that acts as a wrapper for Theano and Tensorflow. With Keras, we can create powerful and complex Deep Learning models with only a few lines of code, which will allow you to have a global vision of what you’re doing.

We will primarily use Scikit-learn, which is the most practical Machine Learning library.
And, of course, we must mention the usual suspects: this entire course is based on Python, and you will gain hours and hours of invaluable hands-on practical coding experience in each and every section.

— Who Should Take This Course? —
As you can see, there are a variety of tools in the Deep Learning space, and in this course, we’ll show you the most important and forward-thinking ones so that when you’re finished with Deep Learning A-ZTM, your skills will be cutting-edge.

If you’ve worked with Deep Learning before, you’ll find this course to be refreshing, inspiring, and very practical. Inside Deep Learning A-ZTM, you’ll learn some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn’t even exist a year ago), and you’ll gain a tremendous amount of valuable hands-on experience with real-world business challenges. Plus, you’ll find inspiration to learn new Deep Learning skills.

Mastering Deep Learning requires not only intuition and tools, but also the ability to apply these models to real-world scenarios and derive actual measurable results for the business or project, which is why we’ve included six exciting challenges in this course:

You will be given a dataset with a large sample of the bank’s customers to solve a data analytics challenge for a bank. To create 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, and so on.

If you complete this project successfully, you will add significant value to the bank, as your Deep Learning model may help the bank reduce customer churn significantly.
#2 Image Recognition
In this section, you’ll build a Convolutional Neural Network that can detect various objects in images. We’ll use this Deep Learning model to recognize a cat or a dog in a set of images, but you can use it to detect anything else, and we’ll show you how.

#3 Stock Price Prediction
You will create one of the most powerful Deep Learning models in this section, and we will even go so far as to say that you will create the Deep Learning model that is closest to “Artificial Intelligence,” because this model will have long-term memory, just like humans.

This section will teach you how to use this super-powerful model, and we’ll put it to the test by trying to predict the real Google stock price, a challenge that Stanford University researchers have already taken on, and we’ll try to do at least as well as they did.

Markets recently published a report on the subject.
This is the first part of Volume 2 – Unsupervised Deep Learning Models, and the business challenge is to detect fraud in credit card applications. You’ll be building a Deep Learning model for a bank, and you’ll be given a dataset with information on customers applying for advanced credit cards.

#5 & 6 Recommender Systems
Good recommender systems are extremely valuable in today’s world, from Amazon product suggestions to Netflix movie recommendations, and experts who can create them are among the highest-paid Data Scientists on the planet.

Your final Recommender System will be able to predict the ratings of movies that customers haven’t seen, and by ranking the predictions from 5 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’ll give ourselves two chances.

The list of movies will be explicit, so all you have to do is rate the movies you’ve already seen, enter your ratings in the dataset, run your model, and voila! The Recommender System will tell you exactly which movies you’d enjoy one night if you’re out of ideas for what to watch on Netflix!

Finally, this is an exciting training program that includes intuition tutorials, practical exercises, and real-life case studies.
We’re really excited about Deep Learning, and we hope to see you in class!
Kirill is a Russian writer who lives in Moscow

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
File Name :Deep Learning A-Z™: Hands-On Artificial Neural Networks Free Download
Content Source:udemy
Genre / Category:Data Science
File Size :6.21 gb
Publisher :Kirill Eremenko
Updated and Published:24 Oct,2021

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File name: Deep-Learning-A-Z™-Hands-On-Artificial-Neural-Networks.rar
File Size:6.21 gb
Course duration:9 hours
Instructor Name:Kirill Eremenko , Hadelin de Ponteves , Ligency Team
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