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
Hyperparameter Tuning Strategies
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A fundamental textbook providing a thorough mathematical and conceptual background of deep learning, with sections dedicated to optimization, regularization, and hyperparameter selection.
- Random Search for Hyper-Parameter Optimization, James Bergstra and Yoshua Bengio, 2012 Journal of Machine Learning Research, Vol. 13 (JMLR Editorial Board) - The original paper introducing random search as a more efficient approach to hyperparameter optimization compared to grid search, particularly effective when only a subset of hyperparameters significantly affects performance.
- Practical Bayesian Optimization of Machine Learning Algorithms, Jasper Snoek, Hugo Larochelle, Ryan P. Adams, 2012 Advances in Neural Information Processing Systems 25, Vol. 25 (NeurIPS Proceedings) - An influential paper demonstrating the efficacy of Bayesian optimization for tuning machine learning algorithms, offering practical advice and empirical results on its application.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - A widely-used practical guide covering various hyperparameter tuning methods, including grid search and random search, with concrete examples and hands-on advice. 3rd edition.