Deep Learning with PyTorch: From Tensors to GANs: Free Course

About this course

Study Deep Learning with PyTorch: From Tensors to GANs through this free English video course from freeCodeCamp. The classroom brings together all 6 public recordings from the selected source curriculum, totaling approximately 12h 5m. 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

• Use tensors and automatic differentiation
• Explain regression and classification training
• Track data through a deep neural network
• Follow convolutional image-classification examples
• Compare regularization and augmentation choices
• Explain the roles of a GAN generator and discriminator

Skills you’ll gain

• PyTorch
• Neural networks
• CNNs
• GANs

Course Overview

Explicit complete Parts 1–6 of 6, ending with generative adversarial networks.

The source lesson list includes “Deep Learning with PyTorch Live Course – Tensors, Gradient Descent & Linear Regression (Part 1 of 6)”, “Deep Learning with PyTorch Live Course – Image Classification with CNNs (Part 4 of 6)”, “Deep Learning with PyTorch Live Course – GANs for Image Generation (Part 6 of 6)”. 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.

Tensors and learning

Follow Part 1 on tensors, gradients and linear regression. Inspect shapes and intermediate values in a small calculation, then connect a parameter update with the loss being minimized.

Classification and deep networks

Work through Parts 2 and 3 on images, logistic regression and deeper models. Keep the inputs, outputs and evaluation measurements distinct while following the training process.

Convolutional models

Study Parts 4 and 5 on CNNs, residual networks, regularization and augmentation. Explain the purpose of each change and compare the resulting behaviour under the same evaluation setup.

Generative models

Follow the final GAN session. Trace the different roles of generator and discriminator, and explain why their training objectives interact instead of treating generated examples as proof of a universally useful model.

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

Basic Python and familiarity with numerical arrays are useful. PyTorch and a notebook environment support practice; GPU access is optional for viewing and may help with larger recorded experiments.

Put the lessons into practice

Train a small classifier on a manageable public or fictional dataset. Record its inputs, outputs, loss and evaluation method. Compare a simple model with one deliberate architecture or regularization change. Inspect a few errors and explain their pattern. For the GAN material, draw a conceptual training sequence and identify which component receives each signal. Keep the exercise small enough to inspect rather than depending on a long opaque training run.

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: freeCodeCamp. Videos play from the freeCodeCamp channel on YouTube.