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Preface
Chapter 1£ºNeural 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£ºApplying 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¡ªfraudsters don¡¯t sleep
Expeinsight¡ªtransfer£®then cash out
StatisticaI quirks¡ªerrors 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£ºUtiliziting Computer Vision
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Chapter 4£ºUnderstanding Time Series
Chapter 5£ºParising Textual Data with Natural Language
Chapter 6£ºUsing generative Models
Chapter 7£ºReinforcement Learning for Financial Markets
Chapter 8£ºPrivacy,Debugging,and Launching Your Products
Chapter 9£ºFighting Bias
Chapter 10£ºBayesian Infernence and Probabilisitic
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Index