Deep-Learning-ND-Exercises

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Deep-Learning-ND-Exercises

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Notes and coding exercises from the various lessons in Udacity's Deep Learning Nanodegree Foundation

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# Deep Learning Nanodegree Foundation Notes & Exercises
Notes, coding exercises and projects from Udacity's Deep Learning Nanodegree Foundation program.

## Projects

#### **PROJECT 1: Bike Sharing Neural Network** ([Repo](https://github.com/nehal96/Bike-Sharing-Neural-Network))
#### **PROJECT 2: Image Classification Convolutional Neural Network** ([Repo](https://github.com/nehal96/Image-Classification-CovNet))
#### **PROJECT 3: Simpsons Script Generation with Recurrent Neural Networks** ([Repo](https://github.com/nehal96/Simpsons-Script-Generation))
#### **PROJECT 4: English to French Translation with Sequence to Sequence RNN** ([Repo](https://github.com/nehal96/Seq2Seq-Language-Translation))
#### **PROJECT 5: Face Generation DCGAN** ([Repo](https://github.com/nehal96/Face-Generation-DCGAN))

## Lessons

### 1 - Intro to Neural Networks

* **PROJECT 1: Bike Sharing Neural Network** ([Repo](https://github.com/nehal96/Bike-Sharing-Neural-Network))

### 2 - Sentiment Analysis

#### Sentiment Analysis with Andrew Trask

* Framing Problems for Neural Networks ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sentiment%20Analysis/Sentiment%20Analysis%20with%20Andrew%20Trask/1-framing-problems-for-nns.ipynb))
* Creating the Input/Output Data ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sentiment%20Analysis/Sentiment%20Analysis%20with%20Andrew%20Trask/2-creating-input-output-data.ipynb))
* Building the Neural Network ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sentiment%20Analysis/Sentiment%20Analysis%20with%20Andrew%20Trask/3-building-the-nn.ipynb))
* Making Learning Faster by Reducing Noise ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sentiment%20Analysis/Sentiment%20Analysis%20with%20Andrew%20Trask/4-reducing-noise.ipynb))
* Making our Network Train and Run Faster ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sentiment%20Analysis/Sentiment%20Analysis%20with%20Andrew%20Trask/5-making-network-train-faster.ipynb))
* Reducing Noise by Strategically Reducing Noise ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sentiment%20Analysis/Sentiment%20Analysis%20with%20Andrew%20Trask/6-reducing-vocabulary-noise.ipynb))

#### Intro to TFLearn

* Sentiment Analysis with TFLearn ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sentiment%20Analysis/Sentiment%20Analysis%20with%20TFLearn/sentiment-analysis-tflearn.ipynb))
* Handwritten Digit Recognition with TFLearn and MNIST ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sentiment%20Analysis/Handwritten%20Digit%20Recognition%20with%20TFLearn%20and%20MNIST/handwritten-digit-recognition-with-tflearn.ipynb))

### 3 - MiniFlow

* Forward Propagation ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/tree/master/MiniFlow/1%20-%20Forward%20Propagation))
* Learning and Loss ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/tree/master/MiniFlow/2%20-%20Learning%20and%20Loss))
* Linear Transform ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/tree/master/MiniFlow/3%20-%20Linear%20Transform))
* Sigmoid Function ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/tree/master/MiniFlow/4%20-%20Sigmoid%20Function))
* Cost ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/tree/master/MiniFlow/5%20-%20Cost))
* Gradient Descent ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/tree/master/MiniFlow/6%20-%20Gradient%20Descent))

### 4 - Intro to TensorFlow

* Intro to TensorFlow Notes ([Markdown](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Intro%20to%20TensorFlow/intro-to-tensorflow-notes.md)) ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Intro%20to%20TensorFlow/intro-to-tensorflow-notes.ipynb))
* TensorFlow Neural Network Lab ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Intro%20to%20TensorFlow/tf_neural_network_lab.ipynb))

### 5 - Deep Neural Networks

* Deep Neural Networks Notes ([Markdown](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Deep%20Neural%20Networks/tf-deep-neural-networks.md)) ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Deep%20Neural%20Networks/tf-deep-neural-networks.ipynb))

### 6 - Convolutional Neural Networks

* Convolutional Neural Network Notes ([Markdown](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Convolutional%20Neural%20Networks/convolutional-neural-networks-notes.md)) ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Convolutional%20Neural%20Networks/convolutional-neural-networks-notes.ipynb))

* **PROJECT 2: Image Classification Convolutional Neural Network** ([Repo](https://github.com/nehal96/Image-Classification-CovNet))

### 7 - Recurrent Neural Networks

* Recurrent Neural Network Exercise (Anna Karenina Text Generation) ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Recurrent%20Neural%20Networks/Anna%20KaRNNa.ipynb))
* Sentiment Analysis Recurrent Neural Network ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Recurrent%20Neural%20Networks/Sentiemnt-RNN/Sentiment%20RNN.ipynb))

* **PROJECT 3: Simpsons Script Generation with Recurrent Neural Networks** ([Repo](https://github.com/nehal96/Simpsons-Script-Generation))

#### Resources
* [Lecture on RNNs and LSTM by Andrej Karpathy](https://www.youtube.com/watch?v=iX5V1WpxxkY) (CS231n - Stanford)
* [Understanding LTSM Networks by Christopher Olah](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) (Blog)
* [The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy](https://karpathy.github.io/2015/05/21/rnn-effectiveness/) (Blog)
* [A Beginner's Guide to Recurrent Networks and LSTMs](https://deeplearning4j.org/lstm.html) (Blog)
* [Recurrent Neural Networks in TensorFlow](https://www.tensorflow.org/tutorials/recurrent) (Tutorial)
* [RNNs in TensorFlow](http://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html) (Blog)

### 8 - Embeddings and Word2Vec

* Embeddings and Word2Vec (Skip-Gram) Exercise ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Embeddings%20and%20Word2Vec/Skip-Gram%20word2vec.ipynb))

#### Resources
* [Word2Vec Overview: The Skip-Gram Model by Chris McCormick](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/) (Blog)
* [Efficient Estimation of Word Respresentations in Vector Space by Mikolov et al.](https://arxiv.org/pdf/1301.3781.pdf) (Paper) [PDF] - (*original Word2Vec Paper*)
* [Distributed Representations of Words and Phrases and their Compositionality by Mikolov et al.](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) (Paper) [PDF] - (*improvements to orignial paper*)
* [Word2Vec: NLP with Deep Learning with TensorFlow by Thushan Ganegedara](http://www.thushv.com/natural_language_processing/word2vec-part-1-nlp-with-deep-learning-with-tensorflow-skip-gram/) (Blog)
* [TensorFlow Word2Vec Tutorial](https://www.tensorflow.org/tutorials/word2vec) (Tutorial)

### 9 - TensorBoard

* Viewing Graphs ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/TensorBoard/Anna%20KaRNNa.ipynb))
* Name Scopes ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/TensorBoard/Anna%20KaRNNa%20Name%20Scoped.ipynb))
* Inspecting Variables - TF Summaries ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/TensorBoard/Anna%20KaRNNa%20Summaries.ipynb))
* Choosing Hyperparameters ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/TensorBoard/Anna%20KaRNNa%20Hyperparameters.ipynb))

#### Resources
* [Hands-on TensorBoard Tutorial](https://www.youtube.com/watch?v=eBbEDRsCmv4&feature=youtu.be) (TensorFlow Dev Summit 2017)
* [TensorBoard: Visualizing Learning](https://www.tensorflow.org/get_started/summaries_and_tensorboard) (Tutorial)

### 10 - Weight Initialization

* Comparing Different Weight Initializations on MNIST ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Weight%20Initialization/weight_initialization.ipynb))

#### Resources
* [Understanding the Difficulty of Training Deep Feedforward Neural Networks by Bengio et al.](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) (Paper) [PDF]
* [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification by Microsoft Research](https://arxiv.org/pdf/1502.01852v1.pdf) (Paper) [PDF]
* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift by Google Research](https://arxiv.org/pdf/1502.03167v2.pdf) (Paper) [PDF]

### 11 - Transfer Learning

* Transfer Learning with VGGNet ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Transfer-Learning/Transfer_Learning.ipynb))

### 12 - Sequence to Sequence RNN

* Character Sequence to Sequence ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Sequence-to-Sequence/sequence_to_sequence_implementation.ipynb))

* **PROJECT 4: English to French Translation with Sequence to Sequence RNN** ([Repo](https://github.com/nehal96/Seq2Seq-Language-Translation))

### 13 - Reinforcement Learning

* Playing Cart-Pole using Q-Learning ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Reinforcement-Learning/Q-learning-cart.ipynb))

### 14 - Autoencoders

* Simple Autoencoder on MNIST ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Autoencoders/Simple_Autoencoder.ipynb))
* Conventional Autoencoder on MNIST ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Autoencoders/Convolutional_Autoencoder.ipynb))

### 15 - Generative Adversarial Networks

#### Intro to GANs
* Generating Handwritten Digits with GANs ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Generative-Adversarial-Networks/Intro_to_GANs_Exercises.ipynb))

#### Deep Convolutional GANs
* Batch Normalization Lesson ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Batch-Normalization/Batch_Normalization_Lesson.ipynb))
* Batch Normalization Exercise ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Batch-Normalization/Batch_Normalization_Exercises.ipynb))
* Deep Convolutional GAN on Street View House Numbers (SVHN) Dataset ([Notebook](https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/DCGAN/DCGAN.ipynb))
* **PROJECT 5: Face Generation DCGAN** ([Repo](https://github.com/nehal96/Face-Generation-DCGAN))

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