Changed to tick boxes

Author: mogwai

README.md

@@ -8,42 +8,43 @@ This is the curriculum for [this](https://youtu.be/_qjNH1rDLm0) video on Youtube
 
 ## 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)
+- [ ] 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)
+- [ ] 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)
+- [ ] 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
+- [ ] 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)
+- [ ] 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
+- [ ] Watch CS 294 [here](http://rail.eecs.berkeley.edu/deeprlcourse/) 
+- [ ] Build a Deep Q Network using Tensorflow