Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus

Deep Learning A-Z™ 2023 is a comprehensive course on neural networks, AI, and ChatGPT. Learn to create deep learning algorithms in Python, with templates included. Gain intuition and hands-on experience with artificial neural networks, convolutional neural networks, recurrent neural networks, self-organizing maps, boltzmann machines, and autoencoders. This course is suitable for beginners with high school mathematics and basic Python programming knowledge. Join now and become a skilled data scientist in the field of deep learning.

 

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

 

Prerequisites

High school mathematics level

Basic Python programming knowledge

Description

*** As seen on Kickstarter ***

Artificial intelligence is growing exponentially. There is no question about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of specialists and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key job.

In any case, the further AI advances, the more complicated become the problems it necessities to tackle. And only Deep Learning can take care of such complex problems and that's the reason it's at the heart of Artificial intelligence.

--- Why Deep Learning A-Z? - - -

The following are five reasons we think Deep Learning A-Z™ really is unique, and stands out from the horde of other training programs out there:

1. Strong STRUCTURE

The first and most important thing we zeroed in on is giving the course a hearty structure. Deep Learning is extremely broad and complex and to navigate this maze you really want a clear and global vision of it.

That's the reason we assembled 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 observed that this is the best structure for mastering Deep Learning.

2. INTUITION TUTORIALS

Such countless courses and books simply bombard you with the hypothesis, and math, and coding... Be that as it may, they neglect to explain, perhaps, the main part: why you are doing what you are doing. And that's the manner by which this course is so unique. We center around developing an intuitive *feel* for the concepts behind Deep Learning algorithms.

With our intuition tutorials you will be confident that you understand all the methods on an instinctive level. And once you continue to the hands-on coding practices you will see with your own eyes the amount more meaningful your experience will be. This is a game-changer.

3. EXCITING PROJECTS

Are you worn out on courses based on over-utilized, outdated data sets?

Indeed? Well then you're in for a treat.

Inside this class we will chip away at Real-World datasets, to tackle Real-World business problems. (Definitely not the boring iris or digit classification datasets that we find in each course). In this course we will tackle six real-world challenges:

Artificial Neural Networks to take care of a Client Churn problem

Convolutional Neural Networks for Image Recognition

Recurrent Neural Networks to anticipate Stock Prices

Self-Organizing Maps to investigate Fraud

Boltzmann Machines to create a Recomender Framework

Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize

*Stacked Autoencoders is a brand new method in Deep Learning which didn't actually exist several years ago. We haven't seen this strategy explained anywhere else in adequate profundity.

4. HANDS-ON CODING

In Deep Learning A-Z™ we code along with you. Each practical tutorial starts with a blank page and we review the code from scratch. This way you can track and understand exactly the way in which the code meets up and what each line means.

In addition, we will deliberately structure the code in such a way with the goal that you can download it and apply it in your own projects. In addition, we explain bit by bit where and how to change the code to insert YOUR dataset, to tailor the algorithm to your necessities, to get the result that you are after.

This is a course which naturally stretches out into your career.

5. IN-COURSE SUPPORT

Have you at any point taken a course or read a book where you have questions yet cannot reach the author?

Indeed, this course is unique. We are completely dedicated to making this the most problematic and strong Deep Learning course on the planet. With that comes a responsibility to constantly be there when you really want our assistance.

In fact, since we physically also need to eat and rest we have assembled a team of professional Data Researchers to take care of us. At the point when you ask a question you will get a response from us within 48 hours maximum.

Regardless of how complex your inquiry, we will be there. The reality 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 created by Google and is utilized in their discourse recognition framework, in the new google photographs item, gmail, google search and considerably more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more.

PyTorch is as similarly as strong and is being created by researchers at Nvidia and leading colleges: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook.

So which is better and for what?

Indeed, in this course you will have a chance to work with both and understand when Tensorflow is better and when PyTorch is the way to go. All through 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 more than 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 methods.

--- More Tools - - -

Theano is another open source deep learning library. It's basically the same as Tensorflow in its functionality, however by and by we will in any case cover it.

Keras is an incredible library to execute Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create strong and complex Deep Learning models with only a couple of 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.

--- Significantly More Tools - - -

Scikit-learn the most practical Machine Learning library. We will mainly utilize it:

to evaluate the performance of our models with the most relevant strategy, k-Crease Cross Validation

to work on our models with compelling Parameter Tuning

to preprocess our data, so our models can learn in the best conditions

And of course, we have to mention the usual suspects. This entire course is based on Python and in each and every section you will get a really long time of invaluable hands-on practical coding experience.

In addition, all through the course we will utilize Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most effectively.

--- Who Is This Course For? - - -

As you can see, there are bunches of various tools in the space of Deep Learning and in this course we make sure to show you the most important and most moderate ones so that when you're done with Deep Learning A-Z™ your abilities are on the cutting edge of today's innovation.

On the off chance that you are simply starting out into Deep Learning, you will find this course very helpful. Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won't get impeded in unnecessary programming or mathematical intricacies and instead you will apply Deep Learning procedures from early on in the course. You will develop your knowledge starting from the earliest stage you will perceive how with each tutorial you are getting increasingly confident.

Assuming you already have insight with Deep Learning, you will find this course refreshing, inspiring and extremely practical. Inside Deep Learning A-Z™ you will master probably the most cutting-edge Deep Learning algorithms and procedures (some of which didn't actually exist a year ago) and through this course you will gain a monstrous amount of valuable hands-on experience with real-world business challenges. In addition, inside you will find inspiration to investigate new Deep Learning abilities and applications.

--- Real-World Case Studies - - -

Mastering Deep Learning isn't just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and infer actual measurable outcomes for the business or venture. That's the reason in this course we are introducing six exciting challenges:

#1 Churn Modeling Problem

In this part you will settle a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's clients. To make this dataset, the bank gathered information, for example, client id, FICO rating, orientation, age, residency, balance, in the event that the client is active, has a charge card, and so on. During a time of 6 months, the bank noticed in the event that these clients left or stayed in the bank.

Your goal is to make an Artificial Neural Organization that can foresee, based on geo-demographical and transactional information given above, assuming any individual client will leave the bank or stay (client churn). Furthermore, you are asked to rank all the clients of the bank, based on their probability of leaving. To do that, you should utilize the right Deep Learning model, one that is based on a probabilistic approach.

On the off chance that you prevail in this task, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly decrease client churn.

Useful links:

1. Deep Learning A-Z™ 2023 Course
2. Python for Machine Learning
3. Introduction to Neural Networks
4. Convolutional Neural Networks Explained
5. Recurrent Neural Networks in Practice
6. Self-Organizing Maps Tutorial
7. Introduction to Boltzmann Machines
8. Autoencoders: Unleashing the Power of Deep Learning
9. TensorFlow Documentation
10. PyTorch Tutorials