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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

By : David Julian
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

2 (1)
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.
Table of Contents (11 chapters)
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1. Thinking in Machine Learning
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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,
               ...
CONTINUE READING
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