Deep Learning for Programmers 0.11.0 (Clojure AI Book WIP)

[Dragan Djuric]

New release:Deep Learning for Programmers: An Interactive Tutorial with CUDA, OpenCL, MKL-DNN, Java, and Clojure

https://aiprobook.com/deep-learning-for-programmers

+ Chapter on Adaptive Learning Rates

**

no middleman!

100% of the revenue goes towards my open-source work!

** this is the only DL book for programmers

- interactive & dynamic

- step-by-step implementation

- incredible performance, yet no C++ hell (!)

- Intel & AMD CPUs (MKL-DNN)

- Nvidia GPUs (CUDA and cuDNN)

- AMD GPUs (yes, OpenCL too!)

- Clojure (it’s magic!)

- Java Virtual Machine (without Java boilerplate!)

- complete source code

- beautiful typesetting (see the sample chapters)

Current status:

## Table of Contents

### Part 1: Getting Started

4-6 chapters, (TO BE DETERMINED)

### Part 2: Inference ([AVAILABLE])

#### Representing layers and connections ([AVAILABLE])

#### Bias and activation function ([AVAILABLE])

#### Fully connected inference layers ([AVAILABLE])

#### Increasing performance with batch processing ([AVAILABLE])

#### Sharing memory ([AVAILABLE])

#### GPU computing with CUDA and OpenCL ([AVAILABLE]

### Part 3: Learning ([AVAILABLE]

#### Gradient descent and backpropagation ([AVAILABLE]

#### The forward pass ([AVAILABLE]

#### The activation and its derivative ([AVAILABLE]

#### The backward pass ([AVAILABLE]

### Part 4: A simple neural networks API ([AVAILABLE]

#### Inference API ([AVAILABLE]

#### Training API ([AVAILABLE]

#### Initializing weights ([AVAILABLE]

#### Regression: learning a known function ([AVAILABLE]

### Part 5: Training optimizations (IN PROGSESS)

#### Weight decay ([AVAILABLE]

#### Momentum and Nesterov momentum ([AVAILABLE]

#### Adaptive learning rates ([AVAILABLE]

#### Regression: Boston housing prices (SOON)

#### Dropout (SOON)

#### Stochastic gradient descent (SOON)

#### Classification: IMDB sentiments (SOON)

### Part 6: Tensors (TO BE DETERMINED, BUT SOON ENOUGH)

#### Tensors, Matrices, and ND-arrays (TBD)

#### Tensors on the CPU with MKL-DNN (TBD)

#### Tensors on the GPU with cuDNN (TBD)

#### Tensor API (TBD)

### Part 7: Convolutional layers (TBD)

4-6 Chapters, (TBD)

### Part 8: Recurrent networks (TBD)

4-6 Chapters, (TBD)

[Dragan Djuric]

New release, 0.12.0 is available, with additional chapter on using DL for regression and predicting the prices of Boston real estate (a classic regression example).