Deep Learning Specialization Notes
Annotated PDF notes for the Coursera Deep Learning Specialization.
A browser-friendly collection of deep learning notes covering neural networks, optimization, machine learning strategy, convolutional models, and sequence models. Each PDF opens directly in the browser.
Neural Networks and Deep Learning
Course 1 notes on neural network basics, forward propagation, backpropagation, and core deep learning ideas.
Improving Deep Neural Networks
Course 2 notes on optimization, regularization, initialization, gradient checking, and practical model tuning.
Structuring Machine Learning Projects
Course 3 notes on error analysis, train/dev/test splits, bias and variance, data mismatch, and ML strategy.
Convolutional Neural Networks
Course 4 notes on convolution layers, pooling, detection, face recognition, neural style transfer, and vision tasks.
Sequence Models
Course 5 notes on RNNs, GRUs, LSTMs, word embeddings, attention, transformers, and NLP applications.