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Sequences & Transformersfirst principles.

DEEP LEARNING • FROM NEURONS TO TRANSFORMERS.

Chapter-by-chapter theory notes, step-by-step textbook exercises, video lectures, and SLO-aligned board examination prep for Sequences & Transformers (Deep Learning).

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Neural Networks (18)Training Deep Networks (23)Computer Vision (15)Sequences & Transformers (19)Generative Deep Learning (14)Reinforcement & Frontier (12)
Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

Sequences & Transformers (Deep Learning)

1. Recurrent Neural Networks

1.1 Recurrent Neurons
1.2 Training RNNs
1.3 Forecasting Time Series
1.4 Handling Long Sequences

2. LSTMs, GRUs and 1D Convolutions

2.1 LSTM Cells
2.2 GRU Cells
2.3 1D Convolutions & WaveNet

3. NLP with Deep Learning

3.1 Tokenization
3.2 Word Embeddings
3.3 Text Classification & Sentiment Analysis
3.4 Encoder–Decoder & Beam Search

4. Attention and Transformers

4.1 Attention Mechanisms
4.2 Self-Attention & Multi-Head Attention
4.3 Positional Encodings
4.4 The Transformer Architecture
4.5 Encoder, Decoder & Encoder-Decoder Models

5. Working with Pretrained Transformers

5.1 Hugging Face Transformers
5.2 Fine-Tuning Transformers
5.3 Efficient & Long-Context Transformers
INTERACTIVE LAB • 18 STEM WORKSTATIONS

Bridge High-School Science to Computational AI

Connect textbook physics, mathematics, and computing to real-world artificial intelligence models and symbolic solvers.

scienceOpen Interactive Laboratories→Board MCQs & Past Papers