Neural Networks and Deep Learning: Free Course

About this course

Study Neural Networks and Deep Learning through this free English video course from DeepLearning.AI. The classroom brings together all 43 public recordings from the selected source curriculum, totaling approximately 5h 40m. You can watch the teaching sequence, search the lesson list and keep track of the recordings you have completed. The course focuses on the subject named in its title. Its value comes from following the explanations and making your own small practice attempts, with enough time to inspect the result and explain the decisions involved.

Course at a glance

What you’ll learn

• Explain the structure of a neural-network prediction
• Connect logistic regression with a neural-network model
• Trace derivatives through a computation graph
• Use vectorized representations of training examples
• Compare activation functions and initialization choices
• Track matrix dimensions through forward and backward propagation

Skills you’ll gain

• Neural networks
• Vectorization
• Backpropagation
• NumPy

Course Overview

Complete foundational recorded lecture edition across Weeks 1–4; separate platform labs, quizzes and optional interview supplements are not promised.

The source lesson list includes “Welcome (Deep Learning Specialization C1W1L01)”, “A Note on Python/Numpy Vectors (C1W2L16)”, “What does this have to do with the brain? (C1W4L08)”. The classroom retains their original titles and positions. Introductions, supplementary explanations and complete-course recordings remain present whenever they are part of the source. A repeated recording is retained at each original position, allowing the course to be checked against the published teaching sequence.

Models and supervised learning

Build the vocabulary of inputs, outputs, parameters and layers. Relate the introductory neural-network explanations to supervised learning and identify what is learned from labelled examples.

Logistic regression and vectorization

Follow the cost-function and gradient-descent derivations. Use computation graphs to organize dependencies, and check how vectorization and Python array shapes express the same calculation across multiple examples.

Shallow neural networks

Connect representations, activation functions and output calculations. Work through the backpropagation intuition with a small network, and explain why nonlinearity and appropriate initialization matter to the model.

Deep neural networks

Track dimensions and computations through additional layers. Separate parameters from hyperparameters, connect the forward and backward passes and use the final explanations to describe what deeper representations contribute.

Who is this for?

This course suits learners who want the specific subject described above and who can work at the stated level. It is also useful as a recorded reference when revisiting a concept or comparing a practice attempt with a demonstrated example. Beginners should give the foundational explanations time before moving to more elaborate applications. Experienced learners can use the lesson names to locate a topic, then inspect the surrounding context before applying a technique.

Before you start

Familiarity with Python, basic algebra and arrays is useful. NumPy and a notebook environment support optional practice. Review derivatives and matrix dimensions whenever those ideas become unclear.

Put the lessons into practice

Implement a tiny binary classifier using a small fictional dataset. Write out the shapes of the inputs, weights and predictions before computing a result. Compare a loop-based calculation with its vectorized form. Trace one derivative through a simple computation graph and explain how it changes a parameter update. Keep the network small enough to inspect the forward pass and check intermediate values rather than relying only on a final accuracy score.

Treat this suggested exercise as independent practice provided by WikiFree. Keep a short working note containing your inputs, assumptions, result and one point you still need to check. Revisit the relevant recording when a step is unclear, then attempt a small variation without following the demonstration line by line. A result you can explain and reproduce is a more useful study outcome than simply recognizing a familiar term. The exercise is not submitted to an instructor for grading.

Certificate / Assessment

Watching these public recordings and using WikiFree completion tracking does not award a certificate, academic credit, an examination voucher or instructor assessment. The video curriculum is the material included here. Separate platform assignments, credentials and paid services are not part of video viewing. Use the practice output to review which concepts you can explain, which steps you can repeat independently and which questions need another look at the teaching.

Frequently asked questions

Provider: DeepLearning.AI. Videos play from the DeepLearning.AI channel on YouTube.