## Neural Networks

What is Neural Network? Neuron? Weights? Bias? Forward Propagation? Cost Function? Gradient Descent? Learning Rate? Back propagation? Batches? Epochs? Dropout? Batch Normalisation? CNN? Pooling? Padding? Data Augmentation? Recurrent Neurone? RNN? Vanishing Gradient Problem? Exploding Gradient Problem.

## Introduction to Tensorflow

## Introduction to Convolution Networks

What it is? Why it is there? How it does?

Going a bit deeper, here is an article which goes step by step explanation of

how spatial locations are important,

As now you have an intuition of how Conv Nets works, it’s time to understand few concepts a bit deeper concepts about image processing.

Please check filters from wikipedia.

Understand these questions

- What is a filter? Is it always 2d?
- How does a filter effect output dimensions?
- What is striding?
- What is padding?

As we want to shrink higher dimensional images into lower dimensional feature vectors, we use filters to map these large images. As repeatedly apply filter, we can have these