Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
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Recurrent dropout

Having read this far into the book, you've already encountered the concept of dropout. Dropout removes some elements of one layer of input at random. A common and important tool in RNNs is a recurrent dropout, which does not remove any inputs between layers but inputs between time steps:

Recurrent dropout scheme

Just as with regular dropout, recurrent dropout has a regularizing effect and can prevent overfitting. It's used in Keras by simply passing an argument to the LSTM or RNN layer.

As we can see in the following code, recurrent dropout, unlike regular dropout, does not have its own layer:

model = Sequential()
model.add(LSTM(16, recurrent_dropout=0.1,return_sequences=True,input_shape=(max_len,n_features)))