Just some AI problems to learn about neural networks.

The overall philosophy of this bootcamp is to build up pieces manually before I get to abstract them, meaning I will start with understanding then implementing mathematical foundations before adding them to my toolkit.

1. micrograd.ipynb
   1. Manual graph traversal and backpropogation
   2. Neuron unit
2. jax_xor.ipynb
   1. The first neural network. Solves the xor problem
   2. Matrix multiplications of MLP
   3. Manual data loader and training loop: full batching and SGD
   4. Mean squared error
3. jax_mnist.ipynb
   1. Abstracted data loader
   2. Abstracted optimizer: optax
   3. Softmax_cross_entropy loss

4. jax_imdb_sentiment.ipynb
   1. word embeddings
   2. Sigmoid_binary_cross_entropy loss
   3. Global Average pooling
5. keras_mnist.ipynb
   1. Keras abstraction
6. keras imdb_mlp.ipynb
   1. Functions api and subclassing
   2. Pre-trained models
   3. Validation loss, early stopping
   4. Dropout layers
7. keras_imdb_rnn.ipynb
   1. Recurrent Neural Networks
   2. Long Short Term Memory

7. keras_imdb_encoder.ipynb
   1. Multi-head attention
   2. Encoder block
8. keras_english_spanish.ipynb
   1. Decoder block, full transformer
   2. Causal and padding masks

10. fashion_mnist_cnn.ipynb
    1. Convolution layers
    2. Batch normalization
11. cifar10_cnn.ipynb
    1. Data augmentation
    2. Resnet
    3. Transfer learning

12. pytorch_gpt2.ipynb
    1. Decoder only transformers
    2. Pytorch abstractions
    3. Hugging Face datasets + tokenizers
    4. Running remotely on HPC
13. cifar100_cnn.ipynb
    1. tensorboard: visualizing metrics
    2. distributing training

14. evoformer.ipynb
    1. Flax NNX / Jax again
    2. Einstein Sumnation Notation
    3. Gated Multi-head Attention
    4. Axial Attention
    5. Triangle Attention