# Learn_Deep_Learning_in_6_Weeks

**Repository Path**: adcelero/Learn_Deep_Learning_in_6_Weeks

## Basic Information

- **Project Name**: Learn_Deep_Learning_in_6_Weeks
- **Description**: This is the Curriculum for "Learn Deep Learning in 6 Weeks" by Siraj Raval on Youtube 
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-31
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Learn_Deep_Learning_in_6_Weeks
This is the Curriculum for "Learn Deep Learning in 6 Weeks" by Siraj Raval on Youtube 


## Overview

This is the curriculum for [this](https://youtu.be/_qjNH1rDLm0) video on Youtube by Siraj Raval

## Week 1 - Feedforward Neural Networks and Backpropagation

- [ ] Read Part I of the Deep Learning Book found [here](http://www.deeplearningbook.org/) 
- [ ] Use this cheat sheet to help understand any math notation, found [here](https://www.flickr.com/photos/95869671@N08/40544016221)
- [ ] Watch [Build a Neural Net in 4 Minutes](https://www.youtube.com/watch?v=h3l4qz76JhQ)
- [ ] Read [Neural Net in 11 lines](https://iamtrask.github.io/2015/07/12/basic-python-network/) 
- [ ] Type out the neural network code yourself in a text editor, compile, and run it locally (using no ML libraries)
- [ ] Watch [Backpropagation in 5 minutes](https://www.youtube.com/watch?v=q555kfIFUCM)

## Week 2 - Convolutional Networks

- [ ] Watch the Convolutional Networks Specialization on Coursera, found [here](https://www.coursera.org/learn/convolutional-neural-networks). 
- [ ] Read all 3 lecture notes under Module 2 for Karpathy CNN course found [here](http://cs231n.github.io/)
- [ ] Watch my video on CNNs [here](https://www.youtube.com/watch?v=FTr3n7uBIuE&t=1782s) and [here](https://www.youtube.com/watch?v=cAICT4Al5Ow&t=4s)
- [ ] Write out a simple CNN yourself (using no ML libraries)

## Week 3 - Recurrent Networks

- [ ] Watch the Sequence Models Specialization on Coursera, found [here](https://www.coursera.org/learn/nlp-sequence-models)
- [ ] Watch my videos on recurrent networks, [here](https://www.youtube.com/watch?v=BwmddtPFWtA&t=4s), [here](https://www.youtube.com/watch?v=cdLUzrjnlr4), and [here](https://www.youtube.com/watch?v=9zhrxE5PQgY&t=25s)
- [ ] Read Trask's blogpost on LSTM RNNs found [here](https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/)
- [ ] Write out a simple RNN yourself (using no ML libraries)

## Week 4 - Tooling

- [ ] Watch CS20 (Tensorflow for DL research). Slides are [here](http://web.stanford.edu/class/cs20si/syllabus.html). Playlist is [here](https://www.youtube.com/watch?v=g-EvyKpZjmQ&list=PLDuNt91tg0urwwTQNKyUbncSDvMEl74ww)
- [ ] Watch my intro to tensorflow playlist [here](https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV)
- [ ] Read Keras Example code to quickly understand its structure [here](https://keras.io/getting-started/sequential-model-guide/)
- [ ] Learn which GPU provider is best for you [here](https://medium.com/@rupak.thakur/aws-vs-paperspace-vs-floydhub-choosing-your-cloud-gpu-partner-350150606b39)
- [ ] Write out a simple image classifier using Tensorflow

## Week 5 - Generative Adversarial Network
- [ ] Watch the first 7 videos you see [here](https://www.youtube.com/results?search_query=generative+adversarial+network)
- [ ] Build a GAN using no ML libraries
- [ ] Build a GAN using tensorflow
- [ ] Read this to understand the math of GANs, but don't worry if you dont understand it all. This is the bleeding edge [here](https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html)

## Week 6 - Deep Reinforcement Learning
- [ ] Watch CS 294 [here](http://rail.eecs.berkeley.edu/deeprlcourse/) 
- [ ] Build a Deep Q Network using Tensorflow