Machine learning, deep learning, and artificial intelligence (AI) are rapidly evolving fields that has transformed the way we live, work, and interact with technology. It has become an integral part of many industries, including healthcare, finance, transportation, and more. As the world becomes increasingly data-driven, the ability to extract insights from large datasets has become critical for businesses and individuals alike. In this ongoing book, we explore the fundamentals of machine learning, from the basics of data analysis and modeling to the latest advancements in artificial intelligence. Through practical examples and real-world applications, we provide a comprehensive overview of this exciting and dynamic field.
Chapter 1: Signal Processing Fundamentals
- 1.1 Introduction
- 1.2 Fourier Analysis
- 1.2.1 Fourier Series
- 1.2.2 Fourier Transform (Continuous-Time Signal)
- 1.2.3 Discrete Fourier Transform (DFT)
- 1.2.4 Short-Time Fourier Transform (STFT)
- 1.2.5 Fast Fourier Transform (FFT)
- 1.3 Filtering and Convolution
- 1.3.1 Convolution of Continuous Signals
- 1.3.2 Convolution of Discrete Signals
- 1.3.3 Convolution using Matrix Multiplication
- 1.3.4 Fourier transform and Convolution
- 1.3.5 Filter Design
- 1.3.6 Cross-Correlation
Chapter 2: Machine Learning
- 2.1 Nearest Neighbor
- 2.2 Dimensionality Reduction
- 2.2.1 Principal Component Analysis (PCA)
- 2.2.2 Linear Discriminant Analysis (LDA)
- 2.2.3 Comparison Between PCA and LDA
- 2.2.4 Non-negative Matrix Factorization (NMF)
- 2.2.5 t-Distributed Stochastic Neighbor Embedding (t-SNE)
- 2.3 Clustering
- 2.3.1 k-Means Clustering
- 2.3.2 Hierarchical Clustering
- 2.3.3 DBSCAN Clustering
Next Topics:
- Introduction to Neural Networks
- Multilayer Perceptron (MLP)
- Convolutional Neural Networks (CNN)
- Transfer Learning and Fine-Tuning
- Recurrent Neural Networks (RNN)
- Transformers and Attention Mechanism
- Deep Learning Frameworks
- Training and Optimization Techniques
- Generative Adversarial Networks (GANs)
- Reinforcement Learning
- Autoencoders and Variational Autoencoders
- Graph Neural Networks (GNNs)
- Explainability and Interpretability in Machine Learning
Contributions to the book are highly encouraged. If you notice any errors, have suggestions for improvements, or would like to contribute additional sections or chapters, please feel free to submit pull requests or open issues in the GitHub repository and I will provide you with the latex source code. You can also reach me by email, in English or Hebrew, at mail.
We hope you find this ongoing book informative and insightful. Stay tuned for more chapters and updates!