Module 0B: Deep Learning Fundamentals

A required foundation module covering neural networks, training loops, loss functions, optimizers, embeddings, sequence models, and CNNs.

Syllabus Modules

Module 0B: Deep Learning FundamentalsComplete

Build neural networks from scratch. Understand feedforward, activation functions, loss functions, optimizers, backpropagation, embeddings, sequence models, RNNs, CNNs, and the transition to attention/transformers.

Lessons & Submodules

Neural network intuition Perceptron and MLP Activation functions Loss functions Backpropagation intuition Optimizers Overfitting and regularization Embeddings introduction Sequence models introduction CNN basics Deep learning frameworks From deep learning to transformers

Total Lessons: 12Explore Module

Track Progress

0 / 7Projects Verified

Learning Outcomes

Build feedforward neural networks and custom training loops from scratch
Tune model weights utilizing backpropagation, loss functions, and optimizers
Implement word embeddings and recurrent neural network sequence classifiers
Understand CNN filters and translate deep learning constructs to transformer attention layers

Interview Defense

Derive backpropagation gradient updates mathematically
Explain the limits of RNN sequence modeling and the origin of attention mechanisms