Deep Learning Demystified, A Step-by-Step Introduction to Neural Networks, Shin K., 2024

Deep Learning Demystified, A Step-by-Step Introduction to Neural Networks, Shin K., 2024.
     
    This book is for anyone who wants to understand the basics of neural networks and deep learning. Whether you are a student, a professional, or a hobbyist, this book is designed to help you grasp the foundational concepts of neural networks. If you have little to no prior knowledge of artificial intelligence but are eager to learn, this book will provide you with the necessary tools and understanding to start your journey. Additionally, if you already have some experience with machine learning and want to deepen your knowledge of neural networks, this book will offer valuable insights and practical knowledge.




The Dream of Artificial Intelligence.
For a long time, humans have dreamed of creating machines that can think and communicate like people. This vision of artificial intelligence has been a cornerstone of science fiction for decades, inspiring countless stories and imaginations. The concept of intelligent machines can be traced back to ancient myths and legends, but it wasn’t until the mid-20th century that the field of artificial intelligence (AI) began to take shape as a scientific discipline.

The dream of AI is now becoming a reality, driven by rapid advancements in technology and a deeper understanding of how the human brain functions. AI encompasses a broad range of technologies, including natural language processing, computer vision, robotics, and more. However, at its core, AI relies heavily on Machine Learning (ML) and Deep Learning. These technologies enable computers to learn from data, identify patterns, and make decisions with minimal human intervention.

CONTENTS.
Chapter l: Introduction to Artificial Neural Networks and Perceptrons.
1.1 The Dream of Artificial Intelligence.
1.2 Basic Concepts of Artificial Neural Networks.
Chapter 2: Neurons and Artificial Neurons.
2.1 Structure and Function of Neurons.
2.2 Structure and Function of Artificial Neurons.
2.3 Perceptron.
2.4 Summary.
Chapter 3: Perceptron Learning Algorithm.
3.1 Learning Process of Artificial Neural Networks.
3.2 Learning Algorithm Example.
3.3 Simple Perceptron example in Python with detailed comments.
3.4 Summary.
Chapter 4: Limitations of Perceptron and Multi-Layer Neural Networks.
4.1 Perceptron as a Linear Classifier.
4.2 The Rise of Multi-laver Neural Networks.
4.3 Structure of Multi-layer Neural Networks.
4.4 Summary.
Chapter 5: Activation Functions.
5.1 Limitations of the Step Function.
5.2 Sigmoid Function.
Vanishing Gradient Problem.
Non Zero-Centered Problem.
5.3 Hyperbolic Tangent Function (tanh Function).
5.4 ReLU Activation Function Dying ReLU Problem.
5.5 Leaky ReLU (Leaky Rectified Linear Unit).
5.6 PReLU (Parametric Rectified Linear Unit).
5.7 ELU (Exponential Linear Unit).
5.8 Summary.
Chapter 6: Gradient Descent.
6.1 Definition of Gradient Descent.
6.2 Loss Function.
MSE (Mean Squared Error).
MAE (Mean Absolute Error).
Cross Entropy.
6.3 Gradient Descent.
Definition of Gradient Descent.
Mathematical Expression.
6.4 Summary.
Chapter 7: Backpropagation Algorithm.
7.1 Background of Backpropagation Algorithm.
7.2 Necessity' of the Backpropagation Algorithm.
7.3 Backpropagation.
Step 1: Forward Pass.
Step 2: Loss Calculation.
Step 3: Backpropagation.
7.4 Backpropagation Example Code.
7.5 Summary.
Chapter 8. Applications of Neural Networks.
8.1 Examples of Neural Network Applications in Various Fields.
Image Recognition.
Speech Recognition.
Natural Language Processing, NLP.
8.2 Real-life Cases AlphaGo.
Facebook Face Recognition.
Google Translate.
8.3 Conclusion and Future Challenges.



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