Hands-On Artificial Intelligence for Beginners
Patrick D. Smith更新时间：2021-06-10 19:34:36
最新章节：Leave a review - let other readers know what you thinkcoaverpage
Title Page
Packt Upsell
Why subscribe?
Packt.com
Contributors
About the author
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
Conventions used
Get in touch
Reviews
The History of AI
The beginnings of AI –1950–1974
Rebirth –1980–1987
The modern era takes hold – 1997-2005
Deep learning and the future – 2012-Present
Summary
Machine Learning Basics
Technical requirements
Applied math basics
The building blocks – scalars vectors matrices and tensors
Scalars
Vectors
Matrices
Tensors
Matrix math
Scalar operations
Element–wise operations
Basic statistics and probability theory
The probability space and general theory
Probability distributions
Probability mass functions
Probability density functions
Conditional and joint probability
Chain rule for joint probability
Bayes' rule for conditional probability
Constructing basic machine learning algorithms
Supervised learning algorithms
Random forests
Unsupervised learning algorithms
Basic tuning
Overfitting and underfitting
K-fold cross-validation
Hyperparameter optimization
Summary
Platforms and Other Essentials
Technical requirements
TensorFlow PyTorch and Keras
TensorFlow
Basic building blocks
The TensorFlow graph
PyTorch
Basic building blocks
The PyTorch graph
Keras
Basic building blocks
Wrapping up
Cloud computing essentials
AWS basics
EC2 and virtual machines
S3 Storage
AWS Sagemaker
Google Cloud Platform basics
GCP cloud storage
GCP Cloud ML Engine
CPUs GPUs and other compute frameworks
Installing GPU libraries and drivers
With Linux (Ubuntu)
With Windows
Basic GPU operations
The future – TPUs and more
Summary
Your First Artificial Neural Networks
Technical requirements
Network building blocks
Network layers
Naming and sizing neural networks
Setting up network parameters in our MNIST example
Activation functions
Historically popular activation functions
Modern approaches to activation functions
Weights and bias factors
Utilizing weights and biases in our MNIST example
Loss functions
Using a loss function for simple regression
Using cross-entropy for binary classification problems
Defining a loss function in our MNIST example
Stochastic gradient descent
Learning rates
Utilizing the Adam optimizer in our MNIST example
Regularization
The training process
Putting it all together
Forward propagation
Backpropagation
Forwardprop and backprop with MNIST
Managing a TensorFlow model
Saving model checkpoints
Summary
Convolutional Neural Networks
Overview of CNNs
Convolutional layers
Layer parameters and structure
Pooling layers
Fully connected layers
The training process
CNNs for image tagging
Summary
Recurrent Neural Networks
Technical requirements
The building blocks of RNNs
Basic structure
Vanilla recurrent neural networks
One-to-many
Many-to-one
Many-to-many
Backpropagation through time
Memory units – LSTMs and GRUs
LSTM
GRUs
Sequence processing with RNNs
Neural machine translation
Attention mechanisms
Generating image captions
Extensions of RNNs
Bidirectional RNNs
Neural turing machines
Summary
Generative Models
Technical requirements
Getting to AI – generative models
Autoencoders
Network architecture
Building an autoencoder
Variational autoencoders
Structure
Encoder
Decoder
Training and optimizing VAEs
Utilizing a VAE
Generative adversarial networks
Discriminator network
Generator network
Training GANs
Other forms of generative models
Fully visible belief nets
Hidden Markov models
Boltzmann machines
Summary
References
Reinforcement Learning
Technical requirements
Principles of reinforcement learning
Markov processes
Rewards
Policies
Value functions
The Bellman equation
Q–learning
Policy optimization
Extensions on policy optimization
Summary
Deep Learning for Intelligent Agents
Technical requirements
Word embeddings
Word2vec
Training Word2vec models
GloVe
Constructing a basic agent
Summary
Deep Learning for Game Playing
Technical requirements
Introduction
Networks for board games
Understanding game trees
AlphaGo and intelligent game-playing AIs
AlphaGo policy network
AlphaGo value network
AlphaGo in action
Networks for video games
Constructing a Deep Q–network
Utilizing a target network
Experience replay buffer
Choosing action
Training methods
Training the network
Running the network
Summary
Deep Learning for Finance
Requirements
Introduction to AI in finance
Deep learning in trading
Building a trading platform
Basic trading functions
Creating an artificial trader
Managing market data
Price prediction utilizing LSTMs
Backtesting your algorithm
Event-driven trading platforms
Gathering stock price data
Generating word embeddings
Neural Tensor Networks for event embeddings
Predicting events with a convolutional neural network
Deep learning in asset management
Summary
Deep Learning for Robotics
Technical requirements
Introduction
Setting up your environment
MuJoCo physics engine
Downloading the MuJoCo binary files
Signing up for a free trial of MuJoCo
Configuring your MuJoCo files
Installing the MuJoCo Python package
Setting up a deep deterministic policy gradients model
Experience replay buffer
Hindsight experience replay
The actor–critic network
The actor
The critic
Deep Deterministic Policy Gradients
Implementation of DDPG
Summary
References
Deploying and Maintaining AI Applications
Technical requirements
Introduction
Deploying your applications
Deploying models with TensorFlow Serving
Utilizing docker
Building a TensorFlow client
Training and deploying with the Google Cloud Platform
Training on GCP
Deploying for online learning on GCP
Using an API to Predict
Scaling your applications
Scaling out with distributed TensorFlow
Testing and maintaining your applications
Testing deep learning algorithms
Summary
References
Other Books You May Enjoy
Leave a review - let other readers know what you think
更新时间：2021-06-10 19:34:36