Julia for Deep Learning
Chapter 1: Foundations of Julia for Machine Learning
Julia's Edge in Computationally Intensive Tasks
Type System and Multiple Dispatch in Machine Learning Contexts
Essential Julia Packages for Data Science
Numerical Computation with Julia: Arrays and Linear Algebra
Automatic Differentiation: The Core Mechanism
Overview of Julia's Machine Learning Ecosystem
Setting Up Your Julia Deep Learning Environment
Practice: Julia for Data Manipulation and Basic Algorithms
Chapter 2: Introduction to Flux.jl for Deep Learning
Flux.jl: Design Principles and Architecture
Flux.jl Primitives: Layers, Models, and Chains
Defining Simple Neural Network Layers
Working with Activation Functions in Flux
Loss Functions: Measuring Model Error
Optimizers: Guiding the Learning Process
Zygote.jl: Automatic Differentiation in Flux
Constructing a Basic Neural Network in Flux
Hands-on Practical: A Simple Regressor with Flux
Chapter 3: Constructing Neural Network Architectures
Data Preparation and Preprocessing in Julia
Handling Datasets: Iterators and Loaders with MLUtils.jl
Building Multilayer Perceptrons (MLPs)
Convolutional Neural Networks (CNNs) with Flux
Recurrent Neural Networks (RNNs) and LSTMs in Flux
Working with Embeddings for Sequential Data
Custom Layer Creation in Flux
Model Serialization: Saving and Loading Flux Models
Practice: Implementing a CNN for Image Classification
Chapter 4: Training and Evaluating Deep Learning Models
Dissecting the Model Training Loop
Batching and Epochs in Model Training
Using Callbacks for Training Oversight
Common Evaluation Metrics for Classification and Regression
Applying Regularization: Dropout and Weight Decay
Hyperparameter Tuning Strategies
Visualizing Training Progress and Model Performance
Debugging Flux Models and Training Processes
Hands-on Practical: Training and Fine-tuning a Model
Chapter 5: Advanced Topics and GPU Computing
GPU Acceleration with CUDA.jl and Flux
Managing Data on the GPU
Profiling and Optimizing Flux Model Performance
Working with Pre-trained Models in Julia
Introduction to Generative Models with Flux
A Brief Look at Other Julia Deep Learning Libraries
Interoperability: Calling Python Libraries from Julia for DL
Deployment Pathways for Julia Deep Learning Applications
Practice: Accelerating Training with GPUs
Type System and Multiple Dispatch in Machine Learning Contexts
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- The Julia Language Manual: Types, The Julia Language Developers, 2024 - Provides the authoritative description of Julia's type system, including abstract, concrete, and parametric types, which are central to performance and flexibility in machine learning.
- The Julia Language Manual: Methods, The Julia Language Developers, 2024 - Explains Julia's multiple dispatch mechanism, detailing how function calls are resolved based on the types of all arguments, fundamental for Julia's expressive power and high performance in machine learning.
- Julia: A Fresh Approach to Numerical Computing, Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B. Shah, 2017 SIAM Review, Vol. 59 (Society for Industrial and Applied Mathematics) DOI: 10.1137/141000671 - The foundational paper introducing the Julia language, outlining its design principles and how the type system and multiple dispatch address the 'two-language problem' in scientific computing, directly relevant to machine learning performance.
- Universal Differential Equations for Scientific Machine Learning, Christopher Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lola Dawson, and Keno Fischer, 2021 Journal of Machine Learning Research, Vol. 22 (Machine Learning Research) DOI: 10.5555/3507936.3508000 - This paper showcases how Julia's type system and multiple dispatch are leveraged to build flexible and high-performance scientific machine learning solutions, illustrating their impact in advanced ML research.