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Machine Learning for Finance

You're reading from   Machine Learning for Finance Principles and practice for financial insiders

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789136364
Length 456 pages
Edition 1st Edition
Languages
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Authors (2):
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Jannes Klaas Jannes Klaas
Author Profile Icon Jannes Klaas
Jannes Klaas
James Le James Le
Author Profile Icon James Le
James Le
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Table of Contents (15) Chapters Close

Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
1. Neural Networks and Gradient-Based Optimization 2. Applying Machine Learning to Structured Data FREE CHAPTER 3. Utilizing Computer Vision 4. Understanding Time Series 5. Parsing Textual Data with Natural Language Processing 6. Using Generative Models 7. Reinforcement Learning for Financial Markets 8. Privacy, Debugging, and Launching Your Products 9. Fighting Bias 10. Bayesian Inference and Probabilistic Programming Index

Attention


Are you paying attention? If so, certainly not to everyone equally. In any text, some words matter more than others. An attention mechanism is a way for a neural network to focus on a certain element in a sequence. Focusing, for neural networks, means amplifying what is important:

An example of an attention mechanism

Attention layers are fully connected layers that take in a sequence and output the weighting for a sequence. The sequence is then multiplied with the weightings:

def attention_3d_block(inputs,time_steps,single_attention_vector = False):
    input_dim = int(inputs.shape[2])                             #1
    a = Permute((2, 1),name='Attent_Permute')(inputs)            #2
    a = Reshape((input_dim, time_steps),name='Reshape')(a)       #3
    a = Dense(time_steps, activation='softmax', name='Attent_Dense')(a) # Create attention vector            #4
    if single_attention_vector:                                  #5
        a = Lambda(lambda x: K.mean(x, axis=1), name='Dim_reduction...
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