Deep Learning A-Z: Hands-On Artificial Neural Networks (Udemy.com)

Udemy offers this 22-hour course on neural networks. It covers how neural networks work, the many kinds of neural networks, designing and training networks, Boltzmann Machines and AutoEndcoders.

Created by: Kirill Eremenko

Produced in 2020

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Overall Score : 86 / 100

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Course Description

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*** 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. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering 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
Are you tired of courses based on over-used, outdated data sets?
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-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. 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 pr

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Pros

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Cons

    • Course is a great first look at neural networks.
    • Course takes time to develop an intuitive understanding of neural networks to broaden students’ approaches to design.
    • Even students with limited coding backgrounds have succeeded in this course.
    • If you aren’t good at Python, you will have to become good at it, and on your own time.
    • Course leaves a large number of AI strategies and philosophies off the table.
    • Course is math-heavy and that can prove a barrier for some students.

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Instructor Details

Kirill Eremenko

My name is Kirill Eremenko and I am super-psyched that you are reading this!
Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.
From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.
To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by optimizing processes, maximizing efficiency and increasing profitability. Hadelin is also an online entrepreneur who brings educational e-courses to the world on topics such as Machine Learning, Deep Learning, Artificial Intelligence and Blockchain, which have reached over 700,000 subscribers in 204 countries.Hi there,
We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are r

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Reviews

4.3

173 total reviews

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By Sandeep Reddy S on a week ago

This course taught me well on ANN's. The way they organised the course content is amazing. It pretty much covers every thing you need to know about neural networks.

Great introductory course for deep learning.A good choice for intuition building and hands-on approach.

By Subhankar Bhattacharya on 2 weeks ago

It was an extremely exciting course .. Though personally i struggled quite a bit on the boltzmann machines and auto encoders ... but the ANN, CNN and SOMs were absolutely amazing..As a next step, I feel i would need a more holistic and comprehensive course on recommender systems alone...

By Anastasia S on a month ago

The beginning was really great. But the more I watch, the less I like it :-( Some sections are really weak, some episodes were useless (e.g. a video on how edit a Wikipedia page). They have 2 tutors and as a result there is some inconsistency between theoretical parts and practical tasks. I've finished about 75% and dissappointed. Many important questions from students in Q&A section are left without answers. No updates for legacy code or bug fixes. A couple of practical exercises included in this course are incomplete or even incorrect. No PDF summary materials, so if you want to find/refresh something from a lecture you need to re-watch that video again.Also it would be great to have more meaningful comments and better variable names in code base

By Majdi Flah on a month ago

As a researcher, this course has provided a clear introduction and helped me a lot to understand the theoretical concepts in a very simple way.

By Sam Avery on a month ago

The course provides an excellent framework for gaining an intuitive understanding of materials along with some hand-on experience. I am impressed at how quickly I could be brought up to speed on some of the essentials of machine learning, and now I have the solid background needed to move forward with projects!

By Asterios Nastas on 3 months ago

Really enjoyed the course! I wanted to learn more about Deep Learning as an Entry Level Data Scientist. The course exceeded my expectations in many ways as I got a better overall picture of the Deep Learning world and where it's applicable in Data Science.

I really appreciated this course. The explanations were very clear. The combination of intuition and practical tutorials made the concept less complex than it seemed. Thank you very much for the quality of the content.

By Ryan Comeau on 3 weeks ago

Please note: There is some overlap between this and their previous machine learning course. Be aware that the ANN and CNN sections are the same as their machine learning course.That being said it is nice to have a refresher. Overall, excellent and interesting presentation of the material. the pytorch framework needed for the more advanced methods requires more programming knowledge than Keras. Keras is very high level, lots is abstracted from the user compared to the pytorch implimentation.The unsupervised deep learning methods are captivating and expand the already interesting field of deep learning. I recommend this course, but take it slow. After 1.5 hours of intuition lectures, take a break before the coding. Info needs time to sink in.Thanks Super Data Science

By Yaniv Cohen on 4 months ago

It was like a tour in a park, showing you all the functionality and power of deep learning, but without diving into the mathematics equations (which I rather prefer).In general, I think that this course is good for getting known with most of the topics related to deep learning.Additionally, it still provides additional reading if you want to review the mathematics behind each topic.

By Sushant Ahuja on 8 months ago

ANN and CNN concepts I understood properly after taking this course but RNN and Boltzmann concepts are difficult to visualize and difficult to connect theory part and practical part.Thank you for this wonderful course :)

By Billy Spelchan on 2 months ago

The theory portions were excellent but the coding portions seemed to be lacking. Having a brief overview of the libraries and methods that were used would have improved this course.