Completed an online course on 10-714 Deep Learning Systems offered by CMU in order to delve into the internals of PyTorch and TensorFlow, and understand how they function at a fundamental level.

Designed and built a deep learning library called Needle, comparable to a very minimal version of PyTorch or TensorFlow, capable of efficient GPU-based operations, automatic differentiation of all implemented functions, and the necessary modules to support parameterized layers, loss functions, data loaders, and optimizers.

Build a basic softmax regression algorithm, plus a simple two-layer neural network. Create these implementations both in native Python (using the numpy library), and (for softmax regression) in native C/C++.

✅ A basic add function

✅ Loading MNIST data: parse_mnist function

✅Softmax loss: softmax_loss function

✅Stochastic gradient descent for softmax regression

✅SGD for a two-layer neural network

✅Softmax regression in C++

Build a basic automatic differentiation framework, then use this to re-implement the simple two-layer neural network we used for the MNIST digit classification problem in HW0.

✅Implementing forward computation

• ✅PowerScalar
• ✅EWiseDiv
• ✅DivScalar
• ✅MatMul
• ✅Summation
• ✅BroadcastTo
• ✅Reshape
• ✅Negate
• ✅Transpose

✅Implementing backward computation

• ✅EWiseDiv
• ✅DivScalar
• ✅MatMul
• ✅Summation
• ✅BroadcastTo
• ✅Reshape
• ✅Negate
• ✅Transpose

✅Topological sort: allow us to traverse through (forward or backward) the computation graph, computing gradients along the way

✅Implementing reverse mode differentiation

✅Softmax loss

✅SGD for a two-layer neural network

Implement a neural network library in the needle framework.

✅Implement a few different methods for weight initialization

✅Implement additional modules

• ✅Linear: needle.nn.Linear class
• ✅ReLU:needle.nn.ReLU class
• ✅Sequential: needle.nn.Sequential class
• ✅LogSumExp: needle.ops.LogSumExp class
• ✅SoftmaxLoss: needle.nn.SoftmaxLoss class
• ✅LayerNorm1d: needle.nn.LayerNorm1d class
• ✅Flatten: needle.nn.Flatten class
• ✅BatchNorm1d: needle.nn.BatchNorm1d class
• ✅Dropout: needle.nn.Dropout class
• ✅Residual: needle.nn.Residual class

✅Implement the step function of the following optimizers.

• ✅SGD: needle.optim.SGD class
• ✅Adam: needle.optim.Adam class

✅Implement two data primitives: needle.data.DataLoader and needle.data.Dataset

• ✅Transformations: RandomFlipHorizontal function and RandomFlipHorizontal class
• ✅Dataset: needle.data.MNISTDataset class
• ✅Dataloader: needle.data.Dataloader class

✅Build and train an MLP ResNet

• ✅ResidualBlock: ResidualBlock function
• ✅MLPResNet: MLPResNet function
• ✅Epoch: epoch function
• ✅Train Mnist: train_mnist function

Build a simple backing library for the processing that underlies most deep learning systems: the n-dimensional array (a.k.a. the NDArray).

✅Python array operations

• ✅reshape: reshape function
• ✅permute: permute function