Tensorflow 2.0 is here!
Old coupon no longer works. Use this one instead: https://www.udemy.com/course/deep-learning-tensorflow-2/?couponCode=LASTVIP
PLEASE NOTE: VIP material will be removed from Udemy on November 27. If you signed up for the VIP version (using the VIP coupon) and want access beyond that point, you must email me at info [at] lazyprogrammer [dot] me.
If you want the VIP (full) version of the course beyond that date, you now need to purchase the “main” part and the “VIP” part separately. The “main” part can be purchased on Udemy and the “VIP” part can be purchased from: https://deeplearningcourses.com/c/deep-learning-tensorflow-2
I am happy to announce my latest and most massive course yet – Tensorflow 2.0: Deep Learning and Artificial Intelligence.
Guys I am not joking – this really is my most massive course yet – check out the curriculum.
Many of you will be interested in the stock prediction example, because you’ve been tricked by marketers posing as data scientists in the past – I will demonstrate why their results are seriously flawed.
[if you don’t want to read my little spiel just click here to get your VIP coupon: https://www.udemy.com/deep-learning-tensorflow-2/?couponCode=TENSORVIP]
This is technically Deep Learning in Python part 12, but importantly this need not be the 12th deep learning course of mine that you take!
There are quite few important points to cover in this announcement, so let me outline what I will discuss:
A) What’s covered in this course
B) Why there are almost zero prerequisites for this course
C) The VIP content and near-term additions
D) The story behind this course (if you’ve been following my courses for some time you will be interested in this)
What’s covered in this course
As mentioned – this course is massive. It’s going to take you from basic linear models (the neuron) to ANNs, CNNs, and RNNs.
Thanks to the new standardized Tensorflow 2.0 API – we can move quickly.
The theme of this course is breadth, not depth. If you’re looking for heavy theory (e.g. backpropagation), well, I already have courses for those. So there’s no point in repeating that.
We will however go pretty in-depth to ensure that convolution (the main component of CNNs) and recurrent units (the main component of RNNs) are explained intuitively and from multiple perspectives.
These will include explanations and intuitions you have likely not seen before in my courses, so even if you’ve taken my CNN and RNN courses before, you will still want to see this.
There are many applications in this course. Here are a few:
– we will prove Moore’s Law using a neuron
– image classification with modern CNN design and data augmentation
– time series analysis and forecasting with RNNs
Anyone who is interested in stock prediction should check out the RNN section. Most RNN resources out there only look at NLP (natural language processing), including my old RNN course, but very few look at time series and forecasting.
And out of the ones that do, many do forecasting totally wrong!
There is one stock forecasting example I see everywhere, but its methodology is flawed. I will demonstrate why it’s flawed, and why stock prediction is not as simple as you have been led to believe.
There’s also a ton of Tensorflow-specific content, such as:
– Tensorflow serving (i.e. how to build a web service API from a Tensorflow model)
– Distributed training for faster training times (what Tensorflow calls “distribution strategies”)
– Low-level Tensorflow – this has changed completely from Tensorflow 1.x
– How to build your own models using the new Tensorflow 2.0 API
– Tensorflow Lite (how to export your models for mobile devices – iOS and Android) (coming soon)
– Tensorflow.js (how to export your models for the browser) (coming soon)
Why there are almost zero prerequisites for this course
Due to the new standardized Tensorflow 2.0 API, writing neural networks is easier than ever before.
This means that we’ll be able to blast through each section with very little theory (no backpropagation).
All you will need is a basic understanding of Python, Numpy, and Machine Learning, which are all taught in my free Numpy course.
As I always say, it’s free, so you have no excuses!
Tensorflow 2.0 however, does not invalidate or replace my other courses. If you haven’t taken them yet, you should take this course first for breadth, and then take the other courses which focus on individual models (CNNs, RNNs) for depth.
The VIP content and near-term additions
I had so much content in mind for this course, but I wanted to get this into your hands as soon as possible. With Tensorflow 2.0 due to be released any day now, I wanted to give you all a head start.
This field is moving so fast things were changing while I was making the course. Insane!
I’ll be adding more content in the coming weeks, possibly including but not limited to:
– Transfer Learning
– Natural Language Processing
– Recommender Systems
– Reinforcement Learning
For this release, only the VIP version will be available for some time. That is why you do not see the usual Udemy discount.
You may be wondering: Which parts of the content are VIP content, and which are not?
This time, I wanted to do something interesting: it’s a surprise!
The VIP content will be added to a special section called the “VIP Section”, and this will be removed once the course becomes “Non-VIP”.
I will make an announcement well before that happens, so you will have the chance to download the VIP content before then, as well as get access to the VIP content permanently from deeplearningcourses.com.
The story behind this course
Originally, this course was going to be an RNN course only (hence why the RNN sections have so much more content – both time series and NLP).
The reason for this was, my original RNN course was tied to Theano and building RNNs from scratch.
In Tensorflow, building RNNs is completely different. This is unlike ANNs and CNNs which are relatively similar.
Thus, I could never reconcile the differences between the Theano approach and the Tensorflow approach in my original RNN course. So, I decided that simply making a new course for RNNs in Tensorflow would be best.
But lo and behold – Tensorflow was evolving so fast that a new version was about to be released – so I thought, it’s probably best to just cover everything in Tensorflow 2.0!
And that is how this current course came to be.
I hope you enjoy this action-packed course.
I’ll see you in class!