CS231n

CS231n: "Convolutional Neural Networks for Visual Recognition"

My solutions to the assignments to the state-of-the-art course CS231n "Convolutional Neural Networks for Visual Recognition". It was hard, but it is cool.

Framework

During the course, there was a choice between two frameworks: TensorFlow and PyTorch. ~~I decided to follow the TensorFlow track. Therefore no solution is provided for PyTorch. However, it might occur someday in the future.~~ Now, the solutions are provided for both frameworks.

Content of the Assignments (Spring 2017)

There were three assignments during the Spring 2017 version of the course. They all are completed.

1. [Assignment #1]

• understand the basic Image Classification pipeline and the data-driven approach (train/predict stages)
• understand the train/val/test splits and the use of validation data for hyperparameter tuning.
• develop proficiency in writing efficient vectorized code with numpy
• implement and apply a k-Nearest Neighbor (kNN) classifier
• implement and apply a Multiclass Support Vector Machine (SVM) classifier
• implement and apply a Softmax classifier
• implement and apply a Two layer neural network classifier
• understand the differences and tradeoffs between these classifiers
• get a basic understanding of performance improvements from using higher-level representations than raw pixels (e.g. color histograms, Histogram of Gradient (HOG) features)

2. [Assignment #2]

• understand Neural Networks and how they are arranged in layered architectures
• understand and be able to implement (vectorized) backpropagation
• implement various update rules used to optimize Neural Networks
• implement batch normalization for training deep networks
• implement dropout to regularize networks
• effectively cross-validate and find the best hyperparameters for Neural Network architecture
• understand the architecture of Convolutional Neural Networks and train gain experience with training these models on data

3. [Assignment #3]

• understand the architecture of recurrent neural networks (RNNs) and how they operate on sequences by sharing weights over time
• understand and implement both Vanilla RNNs and Long-Short Term Memory (LSTM) RNNs
• understand how to sample from an RNN language model at test-time
• understand how to combine convolutional neural nets and recurrent nets to implement an image captioning system
• understand how a trained convolutional network can be used to compute gradients with respect to the input image
• implement and different applications of image gradients, including saliency maps, fooling images, class visualizations
• understand and implement style transfer
• understand how to train and implement a generative adversarial network (GAN) to produce images that look like a dataset