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Hands-On Deep Learning with Go
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Hands-On Deep Learning with Go
by Darrell Chua, Gareth Seneque
Hands-On Deep Learning with Go
Title Page
Copyright and Credits
Hands-On Deep Learning with Go
About Packt
Why subscribe?
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: Deep Learning in Go, Neural Networks, and How to Train Them
Introduction to Deep Learning in Go
Introducing DL
Why DL?
DL – a history 
DL – hype or breakthrough?
Defining deep learning
Overview of ML in Go 
ML libraries
Word-embeddings in Go 
Naive Bayesian classification and genetic algorithms for Go or Golang
ML for Go 
Machine learning libraries for Golang 
GoBrain
A set of numeric libraries for the Go programming language
Using Gorgonia
The basics of Gorgonia
Simple example – addition
Vectors and matrices
Visualizing the graph
Building more complex expressions
Summary
What Is a Neural Network and How Do I Train One?
A basic neural network
The structure of a neural network
Your first neural network
Activation functions
Step functions
Linear functions
Rectified Linear Units
Leaky ReLU
Sigmoid functions
Tanh
But which one should we use?
Gradient descent and backpropagation
Gradient descent
Backpropagation
Stochastic gradient descent
Advanced gradient descent algorithms
Momentum
Nesterov momentum
RMSprop
Summary
Beyond Basic Neural Networks - Autoencoders and RBMs
Loading data – MNIST
What is MNIST?
Loading MNIST
Building a neural network for handwriting recognition
Introduction to the model structure
Layers
Training
Loss functions
Epochs, iterations, and batch sizes
Testing and validation
Taking a closer look
Exercises
Building an autoencoder – generating MNIST digits
Layers
Training
Loss function
Input and output
Epochs, iterations, and batch sizes
Test and validation
Building an RBM for Netflix-style collaborative filtering
Introduction to RBMs
RBMs for collaborative filtering
Preparing our data – GroupLens movie ratings
Building an RBM in Gorgonia
Summary
Further reading
CUDA - GPU-Accelerated Training
CPUs versus GPUs
Computational workloads and chip design
Memory access in GPUs
Real-world performance
Intel Xeon Phi CPU
NVIDIA Maxwell GPU
Understanding Gorgonia and CUDA
CUDA
Basic Linear Algebra Subprograms
CUDA in Gorgonia
Building a model in Gorgonia with CUDA support
Installing CUDA support for Gorgonia
Linux
Windows
Performance benchmarking of CPU versus GPU models for training and inference
How to use CUDA
CPU results
GPU results
Summary
Section 2: Implementing Deep Neural Network Architectures
Next Word Prediction with Recurrent Neural Networks
Vanilla RNNs
Training RNNs
Backpropagation through time 
Cost function
RNNs and vanishing gradients
Augmenting your RNN with GRU/LSTM units
Long Short-Term Memory units
Gated Recurrent Units
Bias initialization of gates
Building an LSTM in Gorgonia
Representing text data
Importing and processing input
Summary
Further reading
Object Recognition with Convolutional Neural Networks
Introduction to CNNs
What is a CNN?
Normal feedforward versus ConvNet
Layers
Convolutional layer
Pooling layer
Basic structure
Building an example CNN
CIFAR-10
Epochs and batch size
Accuracy
Constructing the layers
Loss function and solver
Test set output
Assessing the results
GPU acceleration
CNN weaknesses
Summary
Further reading
Maze Solving with Deep Q-Networks
What is a DQN?
Q-learning
Optimization and network architecture
Remember, act, and replay!
Solving a maze using a DQN in Gorgonia
Summary 
Further reading
Generative Models with Variational Autoencoders
Introduction to VAEs
Building a VAE on MNIST
Encoding
Sampling
Decoding
Loss or cost function
Assessing the results
Changing the latent dimensions
Summary
Further reading
Section 3: Pipeline, Deployment, and Beyond!
Building a Deep Learning Pipeline
Exploring Pachyderm
Installing and configuring Pachyderm
Getting data into Pachyderm
Integrating our CNN
Creating a Docker image of our CNN
Updating our CNN to save the model
Creating a data pipeline
Interchangeable models
Mapping predictions to models
Using the Pachyderm dashboard
Summary
Scaling Deployment
Lost (and found) in the cloud
Building deployment templates
High-level steps
Creating or pushing Docker images
Preparing your AWS account
Creating or deploying a Kubernetes cluster
Kubernetes
Cluster management scripts
Building and pushing Docker containers
Running a model on a K8s cluster
Summary
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