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Deep Learning Syllabus
A comprehensive study of neural networks and modern deep learning, spanning foundations, CNNs, sequence models, transformers, generative models, and production frameworks. The full Deep Learning syllabus: 6 subjects, 28 chapters and 103 topics, with 308 flashcards written against it.
Deep Learning subjects
Deep Learning has 6 subjects. Each one is listed below with its chapters in syllabus order; open a subject for every topic and sub-topic inside them.
Foundations of Neural Networks
- Machine Learning Prerequisites · 4 topics
- Mathematical Foundations · 4 topics
- The Perceptron and MLPs · 4 topics
- Backpropagation and Training · 4 topics
- Regularization and Generalization · 4 topics
Convolutional Neural Networks
- Convolution Fundamentals · 4 topics
- Classic CNN Architectures · 5 topics
- Object Detection · 4 topics
- Image Segmentation · 4 topics
- Transfer Learning and Fine-Tuning · 3 topics
Sequence Models and Recurrent Networks
- Recurrent Neural Networks · 3 topics
- Gated Recurrent Architectures · 3 topics
- Sequence-to-Sequence Models · 4 topics
- Word Embeddings and Text Representation · 3 topics
Transformers and Attention
- The Transformer Architecture · 4 topics
- Pretrained Language Models · 4 topics
- Large Language Models · 5 topics
- Vision and Multimodal Transformers · 3 topics
- Efficient Attention · 4 topics
Generative Deep Learning
- Autoencoders · 2 topics
- Generative Adversarial Networks · 3 topics
- Diffusion Models · 4 topics
- Autoregressive Generation · 2 topics
Frameworks and Production
- PyTorch · 4 topics
- TensorFlow and Keras · 3 topics
- Training at Scale · 4 topics
- Model Optimization and Deployment · 4 topics
- MLOps and Experimentation · 4 topics
Deep Learning flashcards
308 question-and-answer cards across 6 subject decks, each shown in full on its subject page.
Deep Learning syllabus FAQ
What subjects are in the Deep Learning syllabus?
Deep Learning covers 6 subjects: Foundations of Neural Networks, Convolutional Neural Networks, Sequence Models and Recurrent Networks, Transformers and Attention, Generative Deep Learning and Frameworks and Production.
How many chapters and topics are in the Deep Learning syllabus?
28 chapters and 103 topics, across 6 subjects. Foundations of Neural Networks is the largest subject, with 20 topics in 5 chapters.
Are there Deep Learning flashcards?
Yes — 308 question-and-answer flashcards in 6 subject decks. Each deck is printed in full on its subject page here, and the same cards are in the Examius app with spaced repetition. The free plan covers one exam of your choice, including its flashcards; Examius Pro, a one-time purchase, unlocks every other exam and deck.
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