Installing the SciPy stack
by John Hearty, David Julian, Sebastian Raschka
Python: Deeper Insights into Machine Learning
- Python: Deeper Insights into Machine Learning
- Table of Contents
- Python: Deeper Insights into Machine Learning
- Python: Deeper Insights into Machine Learning
- Credits
- Preface
- 1. Module 1
- 1. Giving Computers the Ability to Learn from Data
- Building intelligent machines to transform data into knowledge
- The three different types of machine learning
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Using Python for machine learning
- Summary
- 2. Training Machine Learning Algorithms for Classification
- 3. A Tour of Machine Learning Classifiers Using Scikit-learn
- Choosing a classification algorithm
- First steps with scikit-learn
- Modeling class probabilities via logistic regression
- Maximum margin classification with support vector machines
- Solving nonlinear problems using a kernel SVM
- Decision tree learning
- K-nearest neighbors – a lazy learning algorithm
- Summary
- 4. Building Good Training Sets – Data Preprocessing
- 5. Compressing Data via Dimensionality Reduction
- 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- 7. Combining Different Models for Ensemble Learning
- 8. Applying Machine Learning to Sentiment Analysis
- 9. Embedding a Machine Learning Model into a Web Application
- 10. Predicting Continuous Target Variables with Regression Analysis
- Introducing a simple linear regression model
- Exploring the Housing Dataset
- Implementing an ordinary least squares linear regression model
- Fitting a robust regression model using RANSAC
- Evaluating the performance of linear regression models
- Using regularized methods for regression
- Turning a linear regression model into a curve – polynomial regression
- Summary
- 11. Working with Unlabeled Data – Clustering Analysis
- 12. Training Artificial Neural Networks for Image Recognition
- Modeling complex functions with artificial neural networks
- Classifying handwritten digits
- Training an artificial neural network
- Developing your intuition for backpropagation
- Debugging neural networks with gradient checking
- Convergence in neural networks
- Other neural network architectures
- A few last words about neural network implementation
- Summary
- 13. Parallelizing Neural Network Training with Theano
- 1. Giving Computers the Ability to Learn from Data
- 2. Module 2
- 1. Thinking in Machine Learning
- 2. Tools and Techniques
- 3. Turning Data into Information
- 4. Models – Learning from Information
- 5. Linear Models
- 6. Neural Networks
- 7. Features – How Algorithms See the World
- 8. Learning with Ensembles
- 9. Design Strategies and Case Studies
- 3. Module 3
- 1. Unsupervised Machine Learning
- 2. Deep Belief Networks
- 3. Stacked Denoising Autoencoders
- 4. Convolutional Neural Networks
- 5. Semi-Supervised Learning
- 6. Text Feature Engineering
- 7. Feature Engineering Part II
- 8. Ensemble Methods
- 9. Additional Python Machine Learning Tools
- 10. Chapter Code Requirements
- A. Biblography
- Index
The SciPy stack consists of Python along with the most commonly used scientific, mathematical, and ML libraries. (visit: scipy.org). These include NumPy, Matplotlib, the SciPy library itself, and IPython. The packages can be installed individually on top of an existing Python installation, or as a complete distribution (distro). The easiest way to get started is using a distro, if you have not got Python installed on your computer. The major Python distributions are available for most platforms, and they contain everything you need in one package. Installing all the packages and their dependencies separately does take some time, but it may be an option if you already have a configured Python installation on your machine.
Most distributions give you all the tools you need, and many come with powerful developer environments. Two of the best are Anaconda (www.continuum.io/downloads) and Canopy (http://www.enthought.com/products/canopy/). Both have free and commercial versions. For reference, I will be using the Anaconda distribution of Python.
Installing the major distributions is usually a pretty painless task.
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