Neural Networks and Deep Learning

Course 1 of 5 in the Deep Learning Specialization by Andrew Ng (DeepLearning.AI).

Topics

Course 1 of 5 in the Deep Learning Specialization by Andrew Ng (DeepLearning.AI).

Notes

Introduction to Deep Learning
deep-learning
neural-networks

What a neural network is, how supervised learning creates value with deep learning, and why scale of data and computation made deep learning take off.

Jul 28, 2026

Logistic Regression as a Neural Network
deep-learning
logistic-regression
neural-networks

Binary classification notation for images, the logistic regression model with the sigmoid, the log loss and cost functions, and gradient descent.

Jul 29, 2026

Derivatives and Computation Graphs
deep-learning
calculus
backpropagation
gradient-descent

Intuition for derivatives as slopes, computation graphs, backpropagation with the chain rule, and gradient descent for logistic regression on m examples.

Jul 29, 2026
 
Python and Vectorization
deep-learning
vectorization
numpy
python

Replacing for loops with NumPy operations, fully vectorized logistic regression, broadcasting, rank 1 array pitfalls, and the maximum likelihood view of the cost.

Jul 30, 2026
 
Lab: Python Basics with NumPy
deep-learning
numpy
python
lab

Sigmoid and its gradient, reshaping images into vectors, row normalization, softmax, and vectorized L1 and L2 loss functions in NumPy.

Jul 30, 2026

Lab: Logistic Regression with a Neural Network Mindset
deep-learning
logistic-regression
numpy
lab

Build a cat classifier from scratch in NumPy: preprocess images, implement propagation and gradient descent, assemble the model, and tune the learning rate.

Jul 30, 2026

Shallow Neural Networks
deep-learning
neural-networks
activation-functions

One hidden layer networks: layer notation, computing the output, vectorizing across examples, and choosing activation functions and their derivatives.

Jul 31, 2026

Gradient Descent for Neural Networks
deep-learning
neural-networks
backpropagation
gradient-descent

Backpropagation equations for a one hidden layer network, the intuition behind their derivation, and why weights must be initialized randomly.

Jul 31, 2026

Lab: Planar Data Classification with One Hidden Layer
deep-learning
neural-networks
backpropagation
numpy
lab

Build your first neural network in NumPy: a one hidden layer tanh network that learns the flower dataset logistic regression cannot fit.

Aug 1, 2026

Deep L-Layer Networks
deep-learning
neural-networks
forward-propagation
notation

Notation for deep neural networks, forward propagation layer by layer, dimension checking for W, b, Z, and A, and why depth helps.

Aug 1, 2026

Building Blocks of Deep Neural Networks
deep-learning
neural-networks
backpropagation
hyperparameters

Forward and backward functions per layer, the cache, the general backpropagation equations, hyperparameters, and the brain analogy.

Aug 1, 2026

Lab: Building Your Deep Neural Network Step by Step
deep-learning
neural-networks
backpropagation
numpy
lab

Implement every function a deep L-layer network needs in NumPy, from initialization through the forward and backward modules to the parameter update.

Aug 2, 2026

Lab: Deep Neural Network for Image Classification
deep-learning
neural-networks
image-classification
numpy
lab

Assemble the step-by-step functions into a 2-layer and a 4-layer cat classifier, train both, and compare them against logistic regression.

Aug 2, 2026
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