🔄 Recurrent Neural Networks (RNN) Introduction Series v1.0

Systematically master the most critical architecture for time series data and sequence processing

Series Overview

This series is a practical educational content consisting of 5 chapters that allows you to learn Recurrent Neural Networks (RNN) progressively from the fundamentals.

RNN is the most important deep learning architecture for sequence data processing in natural language processing, time series forecasting, and speech recognition. By mastering retention of sequence information through recurrent structures, learning long-term dependencies with LSTM/GRU, sequence transformation with Seq2Seq, and focusing on important parts with Attention mechanisms, you can build sequence processing systems ready for practical use. We provide systematic knowledge from basic RNN mechanisms to LSTM, GRU, Seq2Seq, Attention mechanisms, and time series forecasting.

Features:

• ✅ From Basics to Applications: Systematic learning from Vanilla RNN to the latest Attention mechanisms
• ✅ Implementation-Focused: Over 35 executable PyTorch code examples and practical techniques
• ✅ Intuitive Understanding: Understand operational principles through visualization of hidden states and gradients
• ✅ Full PyTorch Compliance: Latest implementation methods using industry-standard frameworks
• ✅ Practical Applications: Application to practical tasks such as machine translation and stock price prediction

Total Study Time: 100-120 minutes (including code execution and exercises)

How to Proceed with Learning

Recommended Learning Order

For Beginners (completely new to RNN):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (all chapters recommended)
- Required Time: 100-120 minutes

For Intermediate Learners (with deep learning experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Required Time: 80-90 minutes

For Specific Topic Enhancement:
- LSTM/GRU: Chapter 2 (focused study)
- Machine Translation: Chapter 3 (focused study)
- Attention: Chapter 4 (focused study)
- Required Time: 20-25 minutes/chapter

Chapter Details

Chapter 1: RNN Basics and Forward Propagation

Difficulty: Beginner to Intermediate
Reading Time: 20-25 minutes
Code Examples: 7

Learning Content

1. Basic RNN Structure - Recurrent connections, role of hidden states
2. Forward Propagation Computation - Sequential processing of time series data, state updates
3. Backpropagation Through Time - BPTT, gradient propagation through time
4. Gradient Vanishing and Exploding Problems - Difficulty in learning long-term dependencies, gradient clipping
5. Vanilla RNN Implementation - Basic RNN implementation with PyTorch

Learning Objectives

• ✅ Understand the recurrent structure of RNN
• ✅ Explain the role of hidden states
• ✅ Understand the BPTT algorithm
• ✅ Explain the causes of gradient vanishing and exploding problems
• ✅ Implement Vanilla RNN with PyTorch

Read Chapter 1 →

Chapter 2: LSTM and GRU

Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 7

Learning Content

1. LSTM Structure - Cell state, gate mechanisms (input, forget, output)
2. LSTM Computational Flow - Role of each gate and information flow
3. GRU Structure - Reset gate, update gate, simplified design
4. Comparison of LSTM and GRU - Performance, computational cost, criteria for selection
5. Implementation with PyTorch - How to use nn.LSTM and nn.GRU

Learning Objectives

• ✅ Understand the gate mechanisms of LSTM
• ✅ Explain the role of cell state
• ✅ Understand the simplified structure of GRU
• ✅ Appropriately choose between LSTM and GRU
• ✅ Implement LSTM/GRU with PyTorch

Read Chapter 2 →

Chapter 3: Seq2Seq

Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 7

Learning Content

1. Encoder-Decoder Architecture - Basic structure of sequence transformation
2. Context Vector - Fixed-length representation of input sequences
3. Application to Machine Translation - Implementation of English-Japanese translation
4. Teacher Forcing - Efficient technique during training
5. Beam Search - Search for better output sequences

Learning Objectives

• ✅ Understand the roles of Encoder-Decoder
• ✅ Explain the limitations of context vectors
• ✅ Understand the effects of Teacher Forcing
• ✅ Implement Seq2Seq with PyTorch
• ✅ Improve inference with beam search

Read Chapter 3 →

Chapter 4: Attention Mechanism

Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 7

Learning Content

1. Principles of Attention Mechanism - Dynamic focusing on important parts
2. Attention Score Computation - Dot product, scaling, Softmax
3. Attention Visualization - Understanding alignment
4. Introduction to Self-Attention - Bridge to Transformer
5. Seq2Seq with Attention - Improving machine translation accuracy

Learning Objectives

• ✅ Understand the operational principles of Attention mechanism
• ✅ Explain the computation method for Attention scores
• ✅ Visualize the effects of Attention
• ✅ Understand the concept of Self-Attention
• ✅ Implement Attention with PyTorch

Read Chapter 4 →

Chapter 5: Time Series Forecasting

Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 7

Learning Content

1. Time Series Data Preprocessing - Normalization, windowing, data splitting
2. Stock Price Prediction - Stock price prediction models using LSTM
3. Weather Forecasting - Handling multivariate time series data
4. Multi-step Forecasting - Recursive prediction, Multi-step Forecasting
5. Evaluation Metrics - MAE, RMSE, MAPE

Learning Objectives

• ✅ Perform time series data preprocessing
• ✅ Build stock price prediction models with LSTM
• ✅ Handle multivariate time series data
• ✅ Implement multi-step forecasting
• ✅ Measure performance with appropriate evaluation metrics

Read Chapter 5 →

Overall Learning Outcomes

Upon completion of this series, you will acquire the following skills and knowledge:

Knowledge Level (Understanding)

• ✅ Explain the recurrent structure of RNN and the mechanism of BPTT
• ✅ Understand the gate mechanisms of LSTM/GRU and long-term dependency learning
• ✅ Explain the Encoder-Decoder architecture of Seq2Seq
• ✅ Understand the principles and effects of Attention mechanism
• ✅ Explain time series forecasting methods and evaluation metrics

Practical Skills (Doing)

• ✅ Implement RNN/LSTM/GRU with PyTorch
• ✅ Implement machine translation with Seq2Seq
• ✅ Implement Attention mechanism
• ✅ Perform time series data preprocessing
• ✅ Build stock price prediction systems with LSTM

Application Ability (Applying)

• ✅ Select appropriate architectures for new sequence processing tasks
• ✅ Address gradient vanishing problems
• ✅ Efficiently implement sequence transformation tasks
• ✅ Evaluate and improve time series forecasting models

Prerequisites

To effectively learn this series, it is desirable to have the following knowledge:

Required (Must Have)

• ✅ Python Basics: Variables, functions, classes, loops, conditional statements
• ✅ NumPy Basics: Array operations, broadcasting, basic mathematical functions
• ✅ Deep Learning Basics: Neural networks, backpropagation, gradient descent
• ✅ PyTorch Basics: Tensor operations, nn.Module, Dataset and DataLoader
• ✅ Linear Algebra Basics: Matrix operations, dot product, shape transformations

Recommended (Nice to Have)

• 💡 Natural Language Processing Basics: Tokenization, vocabulary, embeddings
• 💡 Time Series Analysis Basics: Trends, seasonality, stationarity
• 💡 Optimization Algorithms: Adam, SGD, learning rate scheduling
• 💡 GPU Environment: Basic understanding of CUDA

Recommended Prior Learning: