*** 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.
Instructor Details
Courses : 11
Specification: Deep Learning AZ : HandsOn Artificial Neural Networks

37 reviews for Deep Learning AZ : HandsOn Artificial Neural Networks
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Yoav Gvili –
The course is very well done, is professional and meets the required needs for a basic. I would like to have more intuition on the what and how. Never the less ths could be a good base to start from.
Santiago Sarabia –
cause ur so cool and you teach the coolest stuff on the internet!
Thomas Roseberry –
A lot of the material is repeated from the ML course, however the organization isn’t as well thought out. For instance we download this file structure at the beginning but then it’s really unclear how it is useful.
Zoltan Szabo –
Very good, and practical. I miss a bit more explanation of the mathematical background, but that can be learnt from other sources as well. 🙂
Suraj Patil –
Yes
Pearl Gupta –
it’s really an amazing course.Both the tutors are really awesome.
Darshan U M –
This course is great.I am excited to learn interesting stuffs taught by him. i had taken a z of machine learning course taught by same person and it was great.
Pierre –
This is worth 100% of my investment
Nishan Kundu –
This is a great course to learn the concepts of deep learning.
Shashi Kumar –
good
Lee Heong Lim –
A good course that covered most of the stuff in deep learning, neural network. But do have a hard time understanding the PyTorch used in Volume 2 Unsupervised Deep Learning.
J’Len Dowdy –
Explains deep learning principles with intuitive analogies and provides end to end examples from data preprocessing to execution with clear and concise steps in progressive fashion.
Arnav AGGARWAL –
I have previously done a machine learning A Z course and almost half of the content is repeated in this course. what’s the point of spending money on this course. I should have downloaded it from the torrent
Markus Tenelsen –
finally even I got an rough idea what deep learning is 🙂
Ken –
I learned a lot about the subject. The teacher made everything easy to understand.
Carlos Zambrano Tandazo –
Hasta ahora muy bien explicado y practico
Shaun Claussen –
To early to tell.
Shubhang Dixit –
it was all very good but i am giving 4 stars because there was no transcript.
Konstantinos Iliakis –
A great course so far!
Rudrajit Choudhuri –
Covers all the concepts promised in the preview very well..Definitely a must buy course
Rohit Jain –
Awesome course! Love it!
Lim Meng Yang Joseph –
It would be good if the trainer could elaborate on what the functions do. Take for example the StandardScaler()’s fit transform() and transform() functions. I found myself pausing the tutorial very frequently to search for more information on the Internet just so that I can understand enough in order to continue. This is disruptive to the tutorial. Then again, the tutorials helped to me narrow the scope of information to search for and internalise. This is better for memory retention as opposed to just being spoonfed.
Rithik Ronald –
It’s really a useful course to master Deep leaning .
Arko Datta –
The lectures are really very good, intuitive, and easy to understand as well as implement. The usage of Google colab instead of any offline IDE is lovely, as learning about the colab IDE also helps me to collaborate with my team who are also quarantined, to build projects.
Beckham Ochieng –
some of the sections are somehow outdated but the intuition lectures make it easy to get insights and the tutors are also really good.In general its a good course
Juan Manuel Gal ndez –
Great introduction to deep learning. The course is not heavy on mathematics but oriented to practical applications, with well chosen illustrative examples.
Paranjay Sharma –
Love the easy to understand colour full explanation of concepts
Manu sehgal –
This was amazing !!
Mel Neubert –
the explanations are easy to understand and straight forward. I am taking this course after the ‘Machine Learning A Z: hands on Python’, so it is a great next step to get deeper into deep learning
Seenivasan Ramasubbu –
It is ok for initial learning
Nicholas Johnson –
Loving the course! Clear, relatively concise explanations for a challenging topic. 5 Stars!
Rafael Esteban Ceron –
Amazing experience, I’ve learned a lot
Abhijit Vikash –
!!
Niranjan Ruikar –
The course is very well organized but the process need to be thorough.
teja yakkala –
I expected a mathematical explanation of Gradient descent
Dr.Maitha AlShaiba AlNuaimi –
very clear and enjoyable, i had learn soo much
Indy Diependaele –
Cursus was informatief en goed uitgelegd, vrij makkelijk te volgen en meestal goede praktijk voorbeelden. Ik heb wel veel geleerd. Wel waren enkele delen moeilijk te begrijpen zonder extra uitleg op te zoeken en het voorbeeld voor de RNN en SOM waren minder dan de andere, bij het RNN ligt het aan de case zelf maar was wel nog goed te volgen. Bij de SOM daarentegen kreeg ik geen consistente waarden en in de comments was duidelijk dat ik niet de enige was.