### Why Tensorflow

You have a lot of data which you want to make sense of, learn patterns, but you don't have the necessary expertice to develop algorithms that will learn through the data. Ofcourse you can develop your own machine learning algorithms to make sense of the data. There might be benifits in developing your own algorithms, proprietary being one, but you might have to invest time and money.

What if you have access to ready made machine learning algorithms which you just have to use in your products? Google's Tensorflow offers such tried and tested algorithms using APIs that you just have to call in your programs. All you have to provide is data, and Tensorflow will take care of the intelligence to learn.

2. Increase performance of your models using multiple CPUs and GPUs without change in code.
3. Do numerical computations using data flow graphs.

Check the white paper of Tensorflow at

Install Tensorflow using

Go through Tensorflow tutorials at

Keep following this blog as the researchers at Cerelabs try their hands on Tensorflow...

### Understanding Generative Adversarial Networks - Part II

In "Understanding Generative Adversarial Networks - Part I" you gained a conceptual understanding of how GAN works. In this post let us get a mathematical understanding of GANs.
The loss functions can be designed most easily using the idea of zero-sum games.
The sum of the costs of all players is 0. This is the Minimax algorithm for GANs
Let’s break it down.
Some terminology: V(D, G) : The value function for a minimax game E(X) : Expectation of a random variable X, also equal to its average value D(x) : The discriminator output for an input x from real data, represents probability G(z): The generator's output when its given z from the noise distribution D(G(z)): Combining the above, this represents the output of the discriminator when
given a generated image G(z) as input
Now, as explained above, the discriminator is the maximizer and hence it tries to
maximize

### Understanding Generative Adverserial Networks - Part 1

This is a two part series on understanding Generative Adversarial Networks (GANs). This part deals with the conceptual understanding of GANs. In the second part we will try to understand the mathematics behind GANs.

Generative networks have been in use for quite a while now. And so have discriminative networks. But only in 2014 did someone get the brilliant idea of using them together. These are the generative adversarial networks. This kind of deep learning model was invented by Ian Goodfellow. When we work with data already labelled, it’s called supervised learning. It’s much easier compared to unsupervised learning, which has no predefined labels, making the task more vague.

"Generative Adversarial Networks is the most interesting idea in the last ten years in Machine Learning." - Yann LeCun

In this post, we’ll discuss what GANs are and how they work, at a higher , more abstract level. Since 2014, many variations of the traditional GAN have come out, but the underlying conc…