Machine Learning for Finance: the Practical Guide to Using Data-Driven Algorithms in Banking, Insurance, and Investments

Description

Cover; Copyright; Mapt upsell; Contributors; Table of Contents; Preface; Chapter 1: Neural Networks and Gradient-Based Optimization; Our journey in this book; What is machine learning?; Supervised learning; Unsupervised learning; Reinforcement learning; The unreasonable effectiveness of data; All models are wrong; Setting up your workspace; Using Kaggle kernels; Running notebooks locally; Installing TensorFlow; Installing Keras; Using data locally; Using the AWS deep learning AMI; Approximating functions; A forward pass; A logistic regressor; Python version of our logistic regressor;Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks. You will use machine learning to automate manual tasks, address systematic bias, and find new insights ... Cover Copyright Mapt upsell Contributors Table of Contents Preface Chapter 1: Neural Networks and Gradient-Based Optimization Our journey in this book What is machine learning? Supervised learning Unsupervised learning Reinforcement learning The unreasonable effectiveness of data All models are wrong Setting up your workspace Using Kaggle kernels Running notebooks locally Installing TensorFlow Installing Keras Using data locally Using the AWS deep learning AMI Approximating functions A forward pass A logistic regressor Python version of our logistic regressor Optimizing model parametersMeasuring model loss Gradient descent Backpropagation Parameter updates Putting it all together A deeper network A brief introduction to Keras Importing Keras A two-layer model in Keras Stacking layers Compiling the model Training the model Keras and TensorFlow 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 Making predictions using the heuristic model The F1 score Evaluating with a confusion matrixThe feature engineering approach A feature from intuition -- fraudsters don't sleep Expert insight -- transfer, then cash out Statistical 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 methodsA simple decision tree A random forest XGBoost E2E modeling Exercises Summary Chapter 3: Utilizing Computer Vision Convolutional Neural Networks Filters on MNIST Adding a second filter Filters on color images The building blocks of ConvNets in Keras Conv2D Kernel size Stride size Padding Input shape Simplified Conv2D notation ReLU activation MaxPooling2D Flatten Dense Training MNIST The model Loading the data Compiling and training More bells and whistles for our neural network Momentum The Adam optimizer Regularization L2 regularizationL1 regularization Regularization in Keras Dropout Batchnorm Working with big image datasets Working with pretrained models Modifying VGG-16 Random image augmentation Augmentation with ImageDataGenerator The modularity tradeoff Computer vision beyond classification Facial recognition Bounding box prediction Exercises Summary Chapter 4: Understanding Time Series Visualization and preparation in pandas Aggregate global feature statistics Examining the sample time series Different kinds of stationarity Why stationarity matters Making a time series stationary When to ignore stationarity issues.