DEEP LEARNING FOR BEGINNERS

There is no denying the fact that GANs are awesome! If you don’t know what they are, check out this article where I explain GANs from scratch to a 5-year old and how to implement GANs in Pytorch! In a nutshell, GANs belong to a category of generative models that let us generate incredibly realistic synthetic data, with the same qualities as that of the underlying training data. That means if you feed the model images of a few bedroom decors, after few hours of training it can generate never-seen-before brand-new ideas for your interior design.

Over the past few…

DEEP LEARNING FOR BEGINNERS

Note: Quite frankly, there are already a zillion articles out there explaining the intuition behind GANs. While I will briefly touch upon it, the rest of the article will be an absolute deep dive into the GAN architecture and mainly coding — but with a very very detailed explanation of the pseudocode (open-sourced as an example by PyTorch on Github).

Why do I need GANs?

To put it simply, GANs let us generate incredibly realistic data (based on some existing data). Be it human faces, songs, Simpsons characters, textual descriptions, essay summaries, movie posters — GANs got it all covered! …

What we are doing here is creating the first order difference of a time series ( i.e. value(t) - value(t-1) ) and it is a useful metric used for modelling time series since it denotes the series of changes from one period to the next.

AZURE MINI TUTORIALS

Kaggle is a goldmine of amazing datasets when it comes to machine learning projects. Let’s see how we can load one of them into our ML workspace in the azure portal.

Dataset

As part of this tutorial, we will be loading the Human Faces dataset available on kaggle. This is what I used for training GANs from scratch on custom image data.

Procuring Kaggle API key

Get your Kaggle user name and API key. To create a key:

• Go to your kaggle account → Settings → Account → Create a new API token.
• A kaggle.json file will be downloaded and…

MINI AZURE TUTORIALS

Step 1: Login to Azure ML studio and create a new notebook and select a compute instance to run the notebook.

Step 2: Open the terminal window (next to the magnifying glass icon for searching file names)

Step 3: Create a virtual environment using conda

In the terminal, type the following to create a new environment called newenvtf.

conda create -y --name newenvtf

Step 4: Activate the environment. Again, in the terminal

conda activate newenvtf

You would notice the prompt in terminal changes to (newenvtf) after running the above command:

Deep Learning

In Part 1 on GANs, we started to build intuition regarding what GANs are, why we need them, and how the entire point behind training GANs is to create a generator model that knows how to convert a random noise vector into a (beautiful) almost real image. Since we have already discussed the pseudocode in great depth in Part 1, be sure to check that out as there will be a lot of references to it!

In case you would like to follow along, here is the Github Notebook containing the source code for training GANs using the PyTorch framework.

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DEEP LEARNING FOR BEGINNERS

Disclaimer: I was tempted to write this article mainly because I was unable to find many tutorials that demonstrate how to use AutoKeras with self-collected custom datasets (i.e. datasets other than popular deep learning datasets like MNIST, ImageNet, or CIFAR-10). Additionally, with the latest version of TF rolled out, many functions (used in existing tutorials) are now obsolete and require an update.

TL;DR

1 line of code for people in a hurry:

ImageClassifier(max_trials = 200).fit(x = X, y = y, epochs = 3, validation_split = 0.2)

Introduction

In our previous articles on Deep Learning for Beginners, we learned how to build an…

DEEP LEARNING FOR BEGINNERS

I love Keras, there I said it! However…

As an applied data scientist, nothing gives me more pleasure than quickly whipping up a functional neural network with as little as three lines of code! However, as I have begun to delve deeper into the dark web of neural nets, I would like to accept the fact that Pytorch does allow you a much larger control over the architecture of your network.

Given that most of us are pretty comfortable with Keras (if not, see here for a warm intro to Keras), learning to create a similar network in Pytorch (whilst…