Improving Deep Neural Networks: Tuning, Regularization and Optimization: Free Course

About this course

Study Improving Deep Neural Networks: Tuning, Regularization and Optimization through this free English video course from DeepLearning.AI. The classroom brings together all 34 public recordings from the selected source curriculum, totaling approximately 4h 44m. 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

• Separate bias and variance when evaluating a model
• Explain regularization and dropout in training
• Recognize vanishing or exploding gradients
• Compare mini-batch descent, momentum, RMSProp and Adam
• Choose meaningful scales for hyperparameter searches
• Explain batch normalization and softmax classification

Skills you’ll gain

• Regularization
• Optimization
• Hyperparameter tuning
• Batch normalization

Course Overview

Complete recorded lecture edition covering regularization, optimization and practical tuning; preserve original lesson titles and order.

The source lesson list includes “Train/Dev/Test Sets (C2W1L01)”, “Understanding Exponentially Weighted Averages (C2W2L04)”, “TensorFlow (C2W3L11)”. 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.

Training diagnosis and regularization

Start with train, development and test sets, then connect model behaviour with bias and variance. Compare regularization methods and keep their effect on learning separate from changes in the evaluation setup.

Gradients and checks

Follow the input-normalization, initialization and gradient-checking explanations. Use a small example to understand why numerical checks are helpful and identify what they can reveal about an implementation.

Optimization methods

Compare mini-batches, moving averages, momentum, RMSProp and Adam. Explain which information each update uses, and relate learning-rate choices and decay to the behaviour of the training process.

Practical tuning

Work through search scales, batch normalization and multiclass outputs. Keep a record of the configuration, evaluation criterion and result of each trial so that a tuning decision can be explained and repeated.

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

Prior study of neural-network forward and backward propagation is recommended. Basic Python and array operations are useful. Framework demonstrations belong to the recorded software edition; choose compatible practice dependencies.

Put the lessons into practice

Design a small experiment comparing two optimization choices on the same fictional classification task. Fix the data split and evaluation metric, record the hyperparameters and compare learning behaviour. Add a regularized variant and explain what outcome would suggest overfitting. Write a tuning log that separates a deliberate change from random variation. The goal is a controlled comparison and a clear interpretation, not a promise of a universally best optimizer.

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.