Preface
Chapter 1：Neural Networks and Gradient．Based optimization
Our iourney in this book
What iS machine Iearning?
Supervised Iearning
Unsupervised learning
Reinforcement learning
The unreaS0nabIe effectiveness of data
AIl models are wrong
Setting up your workspace
Using Kaggle kernels
Running notebooks Iocally
Installing TensorFIow
Installing Keras
Using data locally
Using the AWS deep learning AMI
Approximating functions
A forward pass
A logistic regressor
Python version of our Iogistic regressor
optimizing model parameters
Measuring modelloSS
Gradient descent
Backpropaqation
Parameter updates
Putting it all together
A deeper network
A brief introduction to Keras
lmporting Keras
A two-layer modeIin Keras
Stacking layers
Compiling the model
Training the model
Keras and TensorFIow
Tensors and the computational graph
Exercises
Summary
Chapter 2：Applying Maching Learning to Structured Data
The data
Heuristic，feature．based。and E2E models
The machine Iearning software stack
The heuristic approach
Making predictions using the heuristic model
The F1 score
Evaluating with a confusion matrix
The feature engineering approach
A feature from intuition—fraudsters don’t sleep
Expeinsight—transfer．then cash out
StatisticaI quirks—errors in balances
Preparing the data for the Keras library
One-hot encoding
Entity embeddings
Tokenizing categories
Creating input models
Training the model
Creating predictive models with Keras
Extracting the target
Creating a test set
Creating a validation set
Oversampling the training data
Building the model
Creating a simple baseline
Building more complex models
A brief primer on tree-based methods
A simple decision tree
A random forest
XGBoost
E2E modeling
Exercises
Summary
Chapter 3：Utiliziting Computer Vision
……
Chapter 4：Understanding Time Series
Chapter 5：Parising Textual Data with Natural Language
Chapter 6：Using generative Models
Chapter 7：Reinforcement Learning for Financial Markets
Chapter 8：Privacy,Debugging,and Launching Your Products
Chapter 9：Fighting Bias
Chapter 10：Bayesian Infernence and Probabilisitic
Other Books You May Enjoy
Index
近年来，大数据、云计算、人工智能以及区块链等新兴技术的快速发展和应用推动了金融业的巨大变革，金融科技开始渗透传统金融业的各个领域和各个环节，如银行、证券、保险以及前台、中台、后台，传统业务无不受到金融科技的挑战。在诸多新兴技术之中，人工智能和机器学习对传统金融业的冲击愈加明显，也越来越具备颠覆性。人工智能在金融领域的应用具有天然优势，它在处理数据方面有很强的优势，而金融领域本质上就是要处理信息、处理数据，所以人工智能和金融业可以说是天作之合。本书讲解相关知识。
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