更新时间：2021-06-11 13:26:50
封面
版权页
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Contributors
About the author
About the reviewer
Preface
Who this book is for
What this book covers
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Get in touch
Chapter 1. Neural Networks and Gradient-Based Optimization
Our journey in this book
What is machine learning?
Supervised learning
Unsupervised learning
Reinforcement learning
Setting up your workspace
Using Kaggle kernels
Using the AWS deep learning AMI
Approximating functions
A forward pass
A logistic regressor
Optimizing model parameters
Measuring model loss
A deeper network
A brief introduction to Keras
Tensors and the computational graph
Exercises
Summary
Chapter 2. Applying Machine Learning to Structured Data
The data
Heuristic feature-based and E2E models
The machine learning software stack
The heuristic approach
The feature engineering approach
Preparing the data for the Keras library
Creating predictive models with Keras
A brief primer on tree-based methods
E2E modeling
Chapter 3. Utilizing Computer Vision
Convolutional Neural Networks
Filters on color images
The building blocks of ConvNets in Keras
More bells and whistles for our neural network
Working with big image datasets
Working with pretrained models
The modularity tradeoff
Computer vision beyond classification
Chapter 4. Understanding Time Series
Visualization and preparation in pandas
Fast Fourier transformations
Autocorrelation
Establishing a training and testing regime
A note on backtesting
Median forecasting
ARIMA
Kalman filters
Forecasting with neural networks
Conv1D
Dilated and causal convolution
Simple RNN
LSTM
Recurrent dropout
Bayesian deep learning
Chapter 5. Parsing Textual Data with Natural Language Processing
An introductory guide to spaCy
Named entity recognition
Part-of-speech (POS) tagging
Rule-based matching
Regular expressions
A text classification task
Preparing the data
Bag-of-words
Topic modeling
Word embeddings
Document similarity with word embeddings
A quick tour of the Keras functional API
Attention
Seq2seq models
Chapter 6. Using Generative Models
Understanding autoencoders
Visualizing latent spaces with t-SNE
Variational autoencoders
VAEs for time series
GANs
Using less data – active learning
SGANs for fraud detection
Chapter 7. Reinforcement Learning for Financial Markets
