Learn Deep Learning Fundamentals and Practice from Zero
Series Overview
This series is a practical educational content with 5 chapters that teaches Neural Networks progressively from the basics.
Neural Networks are machine learning models that mimic the neurons of the human brain. Starting from simple perceptrons and becoming multilayered, they learn complex patterns and achieve remarkable results in diverse fields including image recognition, natural language processing, and speech recognition.
Features:
- ✅ From Basics to Practice: Systematic learning from perceptrons to latest deep learning frameworks
- ✅ Implementation Focused: Over 60 executable Python code examples, 5 practical projects
- ✅ Balance of Math and Intuition: Emphasizing intuitive understanding beyond just formulas
- ✅ Latest Technology: State-of-the-art implementation methods using PyTorch and TensorFlow
- ✅ Practical Projects: Real-world image classification with MNIST and CIFAR-10
Total Learning Time: 120-140 minutes (including code execution and exercises)
How to Learn
Recommended Learning Path
For Complete Beginners (No ML Knowledge):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (All chapters recommended)
- Duration: 120-140 minutes
For Intermediate Learners (ML Experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 90-110 minutes
Practical Skill Enhancement (Implementation over Theory):
- Chapter 4 (Intensive) → Chapter 5
- Duration: 50-60 minutes
Chapter Details
Chapter 1: Perceptron Basics
Difficulty: Introductory
Reading Time: 20-25 minutes
Code Examples: 9
Learning Content
- What is a Perceptron - The simplest neural network
- Logic Gate Implementation - AND, OR, NAND gates
- Weights and Bias - Meaning and role of parameters
- Linear Separability - Limitations of perceptrons
- XOR Problem - Why multilayer networks are needed
Learning Objectives
- ✅ Understand perceptron structure and operation principles
- ✅ Can implement logic gates in Python
- ✅ Can explain the role of weights and bias
- ✅ Understand the concept of linear separability
- ✅ Know the limitations of single-layer perceptrons
Chapter 2: Multilayer Perceptron and Backpropagation
Difficulty: Beginner to Intermediate
Reading Time: 30-35 minutes
Code Examples: 15
Learning Content
- Multilayer Perceptron (MLP) Structure - Input, hidden, and output layers
- Backpropagation - Core of the learning algorithm
- Gradient Descent - Parameter update method
- Chain Rule - Basics of differentiation
- Complete Implementation - Scratch implementation with NumPy
Learning Objectives
- ✅ Understand and diagram MLP structure
- ✅ Can explain backpropagation mechanism
- ✅ Can implement MLP with NumPy
- ✅ Understand mathematical background of gradient descent
- ✅ Can solve the XOR problem
Chapter 3: Activation Functions and Optimization
Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 12
Learning Content
- Types of Activation Functions - Sigmoid, ReLU, Leaky ReLU, ELU, Swish
- Vanishing Gradient Problem - Challenges of deep networks
- Optimization Algorithms - SGD, Momentum, AdaGrad, Adam, RMSprop
- Learning Rate Adjustment - Learning Rate Scheduling
- Initialization Strategies - Xavier, He initialization
Learning Objectives
- ✅ Understand characteristics and usage of each activation function
- ✅ Can explain vanishing gradient problem and countermeasures
- ✅ Can select appropriate optimization algorithms
- ✅ Can implement learning rate scheduling
- ✅ Understand importance of initialization
Chapter 4: PyTorch and TensorFlow Practice
Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 14
Learning Content
- PyTorch Basics - Tensor, Autograd, nn.Module
- TensorFlow/Keras Basics - Sequential API, Functional API
- Model Building - Custom layers, model definition
- Training Loop - Training, validation, testing
- GPU Utilization - CUDA, acceleration techniques
- Model Save/Load - Checkpoint management
Learning Objectives
- ✅ Understand differences between PyTorch and TensorFlow
- ✅ Can create custom models with nn.Module
- ✅ Can implement complete training loops
- ✅ Can accelerate training with GPU
- ✅ Can save and reuse models
Chapter 5: Image Classification Projects
Difficulty: Intermediate to Advanced
Reading Time: 30-35 minutes
Code Examples: 13
Learning Content
- MNIST Project - Complete implementation of handwritten digit recognition
- Data Preprocessing - Normalization, data augmentation
- CIFAR-10 Project - Color image classification
- Regularization Techniques - Dropout, Batch Normalization, Weight Decay
- Hyperparameter Tuning - Grid Search, Random Search
- Model Evaluation - Confusion Matrix, Accuracy, Recall, F1 Score
Learning Objectives
- ✅ Can achieve 98%+ accuracy on MNIST
- ✅ Can implement MLP on CIFAR-10
- ✅ Can improve generalization with data augmentation
- ✅ Can appropriately use regularization techniques
- ✅ Can evaluate model performance from multiple perspectives
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Can explain the history and basic principles of neural networks
- ✅ Understand mechanisms of perceptron, MLP, and backpropagation
- ✅ Can differentiate and use activation functions and optimization algorithms
- ✅ Can explain vanishing gradient problem and countermeasures
- ✅ Understand differences between PyTorch and TensorFlow
Practical Skills (Doing)
- ✅ Can implement neural networks from scratch with NumPy
- ✅ Can build custom models with PyTorch
- ✅ Can implement complete training loops
- ✅ Can achieve 98%+ accuracy on MNIST
- ✅ Can apply data augmentation and regularization
- ✅ Can tune hyperparameters
Application Ability (Applying)
- ✅ Can design appropriate architectures for new problems
- ✅ Can handle overfitting and training stagnation
- ✅ Can evaluate model performance from multiple perspectives
- ✅ Can advance to sophisticated architectures like CNN and RNN
Let's Get Started!
Are you ready? Start with Chapter 1 and begin your journey into the world of neural networks!
Chapter 1: Perceptron Basics →
Update History
- 2025-10-20: v1.0 Initial Release
Your neural network learning journey starts here!