Book Image

Designing Machine Learning Systems with Python

By : David Julian
Book Image

Designing Machine Learning Systems with Python

By: David Julian

Overview of this book

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.

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Designing Machine Learning Systems with Python
David Julian
Apr, 2016 | 232 pages
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Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Designing Machine Learning Systems with Python
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Thinking in Machine Learning
Thinking in Machine Learning
The human interface
Design principles
Summary
2
Tools and Techniques
Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Summary
3
Turning Data into Information
Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Summary
4
Models – Learning from Information
Models – Learning from Information
Logical models
Tree models
Rule models
Summary
5
Linear Models
Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Summary
6
Neural Networks
Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary
7
Features – How Algorithms See the World
Features – How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Summary
8
Learning with Ensembles
Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Summary
9
Design Strategies and Case Studies
Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Summary
Index
Index

Implementing a neural network

There is one more thing we need to consider, and that is the initialization of our weights. If we initialize them to 0, or all to the same number, all the units on the forward layer will be computing the same function at the input, making the calculation highly redundant and unable to fit complex data. In essence, what we need to do is break the symmetry so that we give each unit a slightly different starting point that actually allows the network to create more interesting functions.

Now, let's look at how we might implement this in code. This implementation is written by Sebastian Raschka, taken from his excellent book, Python Machine Learning, released by Packt Publishing:

import numpy as np
from scipy.special import expit
import sys


class NeuralNetMLP(object):
  
    def __init__(self, n_output, n_features, n_hidden=30,
                 l1=0.0, l2=0.0, epochs=500, eta=0.001, 
                 alpha=0.0, decrease_const=0.0, shuffle=True,
               ...
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