Julia for Machine Learning
Chapter 1: Getting Started with Julia for Machine Learning
Why Julia for Machine Learning? Strengths and Comparisons
Setting Up Your Julia Machine Learning Environment
Julia's Type System and Multiple Dispatch for Scientific Computing
Essential Julia Syntax for Data Operations
Working with Arrays and Matrices in Julia
Introduction to DataFrames.jl
Package Management with Pkg.jl
Hands-on practical: Environment Setup and Basic Data Operations
Chapter 2: Data Manipulation and Preparation in Julia
Loading and Saving Data with DataFrames.jl
Data Cleaning: Handling Missing Values and Outliers
Data Transformation: Scaling, Encoding, and Binning
Feature Engineering Principles
Applying Feature Engineering in Julia
Data Visualization with Plots.jl and Makie.jl
Hands-on practical: Data Cleaning and Feature Creation
Chapter 3: Implementing Supervised Learning Algorithms
Overview of the MLJ.jl Ecosystem
Building and Training Linear Models with MLJ.jl
Implementing Decision Trees and Ensemble Methods
Support Vector Machines (SVMs) using Julia Packages
Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score
Cross-Validation and Hyperparameter Tuning in MLJ.jl
Hands-on practical: Training and Evaluating Supervised Models
Chapter 4: Unsupervised Learning and Dimensionality Reduction
Clustering with K-Means in Julia
Density-Based Clustering: DBSCAN
Principal Component Analysis (PCA) for Dimensionality Reduction
Other Dimensionality Reduction Techniques
Evaluating Clustering Performance
Hands-on practical: Applying Clustering and PCA
Chapter 5: Building Machine Learning Pipelines and Workflows
Introduction to MLJ Pipelines
Constructing Preprocessing and Model Pipelines
Saving and Loading Trained Models and Pipelines
Composing and Tuning Complex Workflows
Strategies for Reproducible ML Experiments in Julia
Hands-on practical: Creating and Managing ML Pipelines
Chapter 6: Introduction to Deep Learning with Flux.jl
Fundamentals of Neural Networks
Getting Started with Flux.jl: Tensors and Layers
Building Feedforward Neural Networks
Defining Loss Functions and Optimizers
Training Neural Networks in Flux.jl
Automatic Differentiation with Zygote.jl
Working with Gradients
Hands-on practical: Building and Training a Simple Neural Network
Fundamentals of Neural Networks
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) DOI: 10.7551/mitpress/9780262035613.001.0001 - A comprehensive and authoritative textbook covering the mathematical and conceptual foundations of deep learning, including neural network basics, architectures, and learning algorithms.
- Neural Networks and Deep Learning, Michael A. Nielsen, 2015 (Determination Press) - An accessible online book providing a clear, intuitive introduction to neural networks and deep learning, building concepts from the ground up with interactive examples.
- Convolutional Neural Networks for Visual Recognition - Course Notes, Stanford University, 2024 (Stanford University) - Excellent introductory material on neural network architectures, activation functions, and the fundamentals of deep learning, widely used for self-study.
- Deep Sparse Rectifier Networks, Xavier Glorot, Antoine Bordes, Yoshua Bengio, 2011 Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 15 DOI: 10.5555/2839210.2839257 - Introduces and analyzes the Rectified Linear Unit (ReLU) activation function, explaining its benefits for training deep neural networks.