Advanced friendly Free

Deep Learning with PyTorch Tutorial

Learn deep learning with PyTorch through tensors, training, CNNs, sequence models, transformers, evaluation, deployment, and responsible operation.

  • 32lessons
  • 8modules
  • ~11hstudy time
  • Advancedstart level
Start the course View curriculum

What you will learn

  • PyTorch Foundations
  • Data Pipelines
  • Training Neural Networks
  • Computer Vision
  • Sequences and Transformers
  • Evaluation and Improvement
  • Efficient and Responsible DL
  • Deployment and Capstone

Course curriculum

8 modules · 32 lessons · follow them in order or jump to any topic

1 PyTorch Foundations 4 lessons
  1. 1Deep Learning and the PyTorch Workflow
  2. 2Tensors, Shapes, Dtypes, and Broadcasting
  3. 3Automatic Differentiation and Computation Graphs
  4. 4Modules, Parameters, Devices, and Reproducibility
2 Data Pipelines 4 lessons
  1. 5Datasets, DataLoaders, Batching, and Shuffling
  2. 6Transforms, Normalization, and Data Augmentation
  3. 7Train, Validation, and Test Splits
  4. 8Imbalanced Data, Sampling, and Label Quality
3 Training Neural Networks 4 lessons
  1. 9Build a Multilayer Perceptron
  2. 10Loss Functions and Task Alignment
  3. 11Optimizers, Learning Rates, and Schedulers
  4. 12Training Loops, Checkpoints, and Early Stopping
4 Computer Vision 4 lessons
  1. 13Convolutional Neural Networks
  2. 14Image Classification Pipeline
  3. 15Transfer Learning and Fine-Tuning
  4. 16Detection, Segmentation, and Vision Evaluation
5 Sequences and Transformers 4 lessons
  1. 17Embeddings and Sequence Representation
  2. 18Recurrent Networks, LSTMs, and GRUs
  3. 19Attention and Transformer Architecture
  4. 20Fine-Tuning a Pretrained Transformer
6 Evaluation and Improvement 4 lessons
  1. 21Metrics, Thresholds, and Calibration
  2. 22Error Analysis and Dataset Slices
  3. 23Regularization, Dropout, Normalization, and Generalization
  4. 24Hyperparameter Search and Experiment Tracking
7 Efficient and Responsible DL 4 lessons
  1. 25Mixed Precision, Gradient Accumulation, and Distributed Training
  2. 26Interpretability and Feature Attribution
  3. 27Bias, Privacy, Robustness, and Adversarial Risk
  4. 28Model Cards, Dataset Documentation, and Governance
8 Deployment and Capstone 4 lessons
  1. 29Save, Load, Export, and Version Models
  2. 30Batch and Online Inference Services
  3. 31Latency, Quantization, Drift, and Monitoring
  4. 32Capstone: Train and Deploy an Image Classifier

About this course

This tutorial suits complete beginners as well as learners who already know some programming and want a structured reference. If Deep Learning with PyTorch is new to you, start from the first lesson and work through each module in order. Experienced learners can use the curriculum above to jump directly to a specific concept.

How to get the most from it

  1. Read each lesson in order and run every example yourself.
  2. Change the examples to test your understanding instead of only copying them.
  3. Complete the exercises and review the common-mistake sections before moving on.
  4. Mark lessons complete as you go — your progress is saved in this browser.

Frequently asked questions

Is this Deep Learning with PyTorch tutorial free?

Yes. Every lesson listed on this page is free to read and can be completed at your own pace.

Do I need previous experience?

No previous experience is required for the introductory lessons. Some advanced modules are easier if you first complete the fundamentals in this course.

How should I practise?

Type and run the examples, deliberately change their inputs, predict the result before execution and build a small project after completing each major module.