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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

Chapter 2: Introduction to Flux.jl for Deep Learning

Sections

2.1 Flux.jl: Design Principles and Architecture
2.2 Flux.jl Primitives: Layers, Models, and Chains
2.3 Defining Simple Neural Network Layers
2.4 Working with Activation Functions in Flux
2.5 Loss Functions: Measuring Model Error
2.6 Optimizers: Guiding the Learning Process
2.7 Zygote.jl: Automatic Differentiation in Flux
2.8 Constructing a Basic Neural Network in Flux
2.9 Hands-on Practical: A Simple Regressor with Flux

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