ML Katas

After tokens have been dispatched to and processed by their respective experts, the outputs need to be combined based on the weights from the gating network. This exercise focuses on this...

Now, let's combine the concepts of dispatching and aggregating into a full, albeit simplified, `torch.nn.Module` for a Mixture of Experts layer. This layer will replace a standard feed-forward...

### Description Instead of modeling a function directly, a Neural ODE models its derivative with a neural network. The output is then found by integrating this derivative over time. [1] Your task...

### Description Model-Agnostic Meta-Learning (MAML) is a meta-learning algorithm that trains a model's initial parameters such that it can adapt to a new task with only a few gradient steps. [1]...

### Description The Transformer architecture is built upon a fundamental component: the Encoder block. [1] Each block is responsible for processing a sequence of embeddings and refining them. Your...

### Description Soft Actor-Critic (SAC) is a state-of-the-art reinforcement learning algorithm known for its stability and sample efficiency. [1] A key component is its critic (or Q-network)...

### Description Neural Cellular Automata (NCA) are a fascinating generative model where complex global patterns emerge from simple, local rules learned by a neural network. [1] A grid of "cells,"...

### Description In a standard neural network, weights are single point estimates. In a Bayesian Neural Network (BNN), we learn a probability distribution over each weight. [1] This allows for...

### Description Your task is to implement a Siamese network that can determine if two images are of the same class, given only one or a few examples of that class at test time. You'll train a...

### Description Solve a simple Ordinary Differential Equation (ODE) using a Physics-Informed Neural Network. A PINN is a neural network that is trained to satisfy both the data and the underlying...

### Description Implement a simple Graph Convolutional Network (GCN) to perform node classification on a graph dataset like Cora. [1] A GCN layer aggregates information from a node's neighbors to...

### Description Implement a simple HyperNetwork. A HyperNetwork is a neural network that generates the weights for another, larger network (the "target network"). [1] This allows for dynamic...

### Description Implement a simple 2D Normalizing Flow model. Normalizing Flows transform a simple base distribution (like a Gaussian) into a more complex distribution by applying a sequence of...

### Description Implement a single Leaky Integrate-and-Fire (LIF) neuron, the fundamental building block of many Spiking Neural Networks (SNNs). Unlike traditional neurons, LIF neurons operate on...

Implement 1D batch normalization manually (without using `nn.BatchNorm1d`). Steps: 1. Compute batch mean and variance. 2. Normalize inputs. 3. Scale and shift with learnable $$\gamma, \beta$$....

Create a deep feedforward net (20 layers, ReLU). Train it on dummy data. Track gradient norms across layers. Observe if gradients explode. Experiment with: - Smaller learning rate. - Gradient...

Implement a Siamese network for MNIST digit similarity: - Two identical CNNs sharing weights. - Contrastive loss function. - Train on pairs of digits (same/different). Evaluate on test pairs.

Implement a single Transformer encoder block: - Multi-head self-attention. - Layer normalization. - Feedforward network. Compare output with `nn.TransformerEncoderLayer`.

Simulate training with `torch.nn.DataParallel`: - Define a simple CNN. - Run it on 2 GPUs (if available). - Verify batch is split across devices. Inspect `model.module` usage.