🧠 Deep Learning Fundamentals Introduction Series v1.0

From Neural Network Basics to Practice - Learn Deep Learning from Scratch

📖 Total Learning Time: 150-180 minutes 📊 Level: Beginner to Intermediate 💻 Code Examples: 40+ 📝 Chapters: 5
🌐 Language: Japanese English

A complete guide to systematically learn the fundamentals of deep learning and understand neural network mechanisms at the implementation level

Series Overview

This series is comprehensive educational content consisting of 5 chapters for acquiring fundamental theory and implementation techniques of deep learning.

Deep Learning is a core technology of artificial intelligence (AI) that has produced revolutionary results in various fields such as image recognition, natural language processing, and speech recognition. In this series, we will learn from the history of deep learning, mathematical foundations of neural networks, learning algorithms, to practical optimization techniques from both theoretical and implementation perspectives.

Features:

Total Learning Time: 150-180 minutes (including code execution and exercises)

What is Deep Learning

Definition and Position

Deep Learning is a method of machine learning that uses multi-layered neural networks to automatically learn features from data.

graph TB A[Artificial Intelligence AI] --> B[Machine Learning ML] B --> C[Deep Learning DL] A2[Rule-based
Expert Systems] --> A B2[Decision Trees
SVM
Random Forest] --> B C2[CNN
RNN
Transformer] --> C style A fill:#e3f2fd style B fill:#fff3e0 style C fill:#f3e5f5

Why "Deep" Learning

"Deep" refers to the depth of layers in neural networks:

As networks become deeper, hierarchical feature representation becomes possible:

Three Elements of Deep Learning

The success of modern deep learning is due to the fusion of the following three elements:

  1. Big Data: With the development of the internet, large amounts of labeled data have become available
  2. Improved Computing Power: Acceleration of parallel computing with GPUs (Graphics Processing Units)
  3. Algorithm Advances: ReLU, Dropout, Batch Normalization, Transformer, etc.

Learning Objectives

After completing this series, you will acquire the following skills:

  1. Historical Understanding of Deep Learning: Flow of development and major breakthroughs
  2. Structure of Neural Networks: Roles of layers, neurons, and activation functions
  3. Understanding Learning Algorithms: Mathematical foundations of gradient descent and backpropagation
  4. Implementation Ability: Building neural networks with NumPy and PyTorch
  5. Optimization Techniques: Regularization, data augmentation, hyperparameter tuning

How to Learn

Recommended Learning Order

graph TD A[Chapter 1: Basic Concepts and History] --> B[Chapter 2: How Neural Networks Work] B --> C[Chapter 3: Learning Algorithms] C --> D[Chapter 4: Regularization and Optimization] D --> E[Chapter 5: Practical Projects] style A fill:#e3f2fd style B fill:#fff3e0 style C fill:#f3e5f5 style D fill:#e8f5e9 style E fill:#fce4ec

🎯 Complete Master Course (All Chapters Recommended)

Target: Deep learning beginners, those who want to systematically learn theory and implementation

How to Proceed: Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5

Time Required: 150-180 minutes

Outcomes: Overall understanding of deep learning, implementation ability, acquisition of practical techniques

⚡ Implementation-Focused Course

Target: Those with basic machine learning knowledge who want to focus on implementation

How to Proceed: Chapter 1 (Overview) → Chapter 2 (Implementation) → Chapter 5 (Practice)

Time Required: 90-100 minutes

Outcomes: Neural network implementation skills, immediately applicable skills

📚 Theory-Focused Course

Target: Those with mathematical background who want to deeply understand theory

How to Proceed: Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4

Time Required: 120-140 minutes

Outcomes: Deepening mathematical understanding, acquisition of optimization theory

Chapter Details

Chapter 1: Basic Concepts of Deep Learning

📖 Reading Time: 30-35 minutes | 💻 Code Examples: 8 | 📝 Exercises: 5

Learning Content

  • Definition and history of deep learning
  • From perceptrons to the modern era
  • Basic structure of neural networks
  • Activation functions (Sigmoid, ReLU, Softmax)
  • Implementation examples in NumPy and PyTorch

Read Chapter 1 →

Chapter 2: How Neural Networks WorkIn Preparation

📖 Reading Time: 35-40 minutes | 💻 Code Examples: 10 | 📝 Exercises: 6

Learning Content

  • Forward propagation calculations
  • Weight matrices and bias vectors
  • Layer connections and computational graphs
  • Loss functions (MSE, Cross Entropy)
  • Implementation of fully connected layers

Chapter 3: Learning AlgorithmsIn Preparation

📖 Reading Time: 35-40 minutes | 💻 Code Examples: 9 | 📝 Exercises: 7

Learning Content

  • Gradient descent
  • Backpropagation
  • Understanding the chain rule
  • Mini-batch learning and SGD
  • Learning rate and number of epochs
  • Visualizing learning

Chapter 4: Regularization and Optimization TechniquesIn Preparation

📖 Reading Time: 30-35 minutes | 💻 Code Examples: 8 | 📝 Exercises: 6

Learning Content

  • Understanding overfitting
  • Regularization methods (L1/L2, Dropout)
  • Batch Normalization
  • Optimization algorithms (Adam, RMSprop)
  • Learning rate scheduling
  • Early stopping

Chapter 5: Practical ProjectsIn Preparation

📖 Reading Time: 40-45 minutes | 💻 Code Examples: 6 | 📝 Exercises: 5

Learning Content

  • Image classification with MNIST dataset
  • Data preprocessing and batch processing
  • Model construction and training loop
  • Performance evaluation and confusion matrix
  • Hyperparameter tuning
  • Model saving and inference

Prerequisites

To make the most of this series, the following prerequisites are recommended:

Required

Recommended

For Beginners: We recommend completing the "Machine Learning Introduction Series" and "Linear Algebra Introduction Series" first.

Frequently Asked Questions (FAQ)

Q1: What is the difference between deep learning and machine learning?

A: Deep learning is a type of machine learning. In conventional machine learning, humans needed to design features, but deep learning automatically learns features from data. By using multi-layered neural networks, it can recognize more complex patterns.

Q2: I'm not good at math, will I be okay?

A: Basic linear algebra (matrix multiplication) and calculus (chain rule) are sufficient. This series explains the meaning of equations intuitively and makes them verifiable with Python code. You can deepen your mathematical understanding while developing implementation skills.

Q3: Is a GPU environment essential?

A: CPU is sufficient for the learning stage. For chapters 1-4 on fundamental learning, small datasets are used, so it works without problems in a CPU environment. For chapter 5 practical projects, we recommend using Google Colab's free GPU.

Q4: Is knowledge of PyTorch or TensorFlow necessary?

A: Not essential. This series first implements neural networks with NumPy to understand the mechanisms, then introduces PyTorch. We emphasize understanding principles over how to use frameworks.

Q5: How much learning time is needed?

A: Total of 150-180 minutes for all chapters. Proceeding at a pace of one chapter per day (30-40 minutes), you can complete it in 5-6 days. Understanding deepens by actually writing code and solving exercise problems while proceeding.

Q6: What should I learn after this series?

A: As next steps, we recommend proceeding to series that learn specific architectures such as "Convolutional Neural Networks (CNN) Introduction", "Recurrent Neural Networks (RNN) Introduction", and "Transformer Introduction". You can also develop practical development skills with "PyTorch Fundamentals Introduction".

Let's Get Started!

Welcome to the world of deep learning. Start with Chapter 1 and understand the core technology of AI from scratch!

← Machine Learning Top Chapter 1: Basic Concepts and History →

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Take the first step towards becoming a future AI engineer with deep learning!

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