Deep Learning

Neural networks and how they're trained: backpropagation, CNNs, RNNs, transformers and PyTorch.

advanced~55 hours13 topics~33h of lessonsRoadmap Level 5

ML turns data into predictions; knowing how models are built and evaluated lets you use them honestly.

RECOMMENDED BEFORE THIS
○ Machine Learning○ Linear Algebra

These are suggested first. You can still open any topic — this only helps you pick an order.

Prerequisites:Machine LearningLinear Algebra
Learning Path
07 stages
13 topics · 7 stages0/13 completed
01

Fundamentals

Start from zero. Build the vocabulary.

Neurons and Neural Networks~75 minintermediateBackpropagation~120 minadvancedPyTorch Basics: Tensors and Autograd~90 minintermediate
02

Core Concepts

The ideas you cannot skip.

Training NNs: Losses, Optimizers, Epochs~90 minintermediateConvolutional Neural Networks~120 minadvancedRNNs and Sequence Models~90 minadvanced
03

Intermediate

Connect ideas and go deeper.

Regularization for Deep Nets~75 minadvancedTransfer Learning~75 minintermediate
04

Advanced

Beyond the basics.

Transformers and Attention~120 minadvancedGenerative Models Overview~75 minadvanced
05

Practice

Solve problems until it sticks.

Practice: Train and Improve~300 minadvanced
06

Projects

Apply everything in real work.

Project: End-to-End DL App~600 minadvanced
07

Interview Prep

Convert knowledge into offers.

Deep Learning Interview Questions~120 minadvanced