You might be wondering why we did not use regular gradient descent but mini-batch learning to train our neural network for the handwritten digit classification. You may recall our discussion on stochastic gradient descent that we used to implement online learning. In online learning, we compute the gradient based on a single training example 
at a time to perform the weight update. Although this is a stochastic approach, it often leads to very accurate solutions with a much faster convergence than regular gradient descent. Mini-batch learning is a special form of stochastic gradient descent where we compute the gradient based on a subset 
of the 
training samples with 
. Mini-batch learning has the advantage over online learning that we can make use of our vectorized implementations to improve computational efficiency. However, we can update the weights much faster than in regular gradient descent. Intuitively, you can think of mini-batch learning as predicting...
Python Machine Learning
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Python Machine Learning
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Overview of this book
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.
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Table of Contents (21 chapters)
Python Machine Learning
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Giving Computers the Ability to Learn from Data
Training Machine Learning Algorithms for Classification
A Tour of Machine Learning Classifiers Using Scikit-learn
Building Good Training Sets – Data Preprocessing
Compressing Data via Dimensionality Reduction
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Embedding a Machine Learning Model into a Web Application
Predicting Continuous Target Variables with Regression Analysis
Working with Unlabeled Data – Clustering Analysis
Training Artificial Neural Networks for Image Recognition
Parallelizing Neural Network Training with Theano
Index
Customer Reviews