A Quick Introduction to Neural Networks

A Quick Introduction...

An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this blog post we will try to […]

Scikit-learn Tutorial: Statistical-Learning for Scientific Data Processing

Scikit-learn Tutoria...

Zip file for off-line browsing: https://github.com/GaelVaroquaux/scikit-learn-tutorial/zipball/gh-pages Statistical learning Machine learning is a technique with a growing importance, as the size of the datasets experimental sciences are facing is rapidly growing. Problems it tackles range from building a prediction function linking different observations, to classifying observations, or learning the structure in an unlabeled dataset. This tutorial […]

5. Putting it all together

5. Putting it all to...

5.1. Pipelining We have seen that some estimators can transform data, and some estimators can predict variables. We can create combined estimators: >>> from scikits.learn import linear_model, decomposition, datasets >>> logistic = linear_model.LogisticRegression() >>> pca = decomposition.PCA() >>> from scikits.learn.pipeline import Pipeline >>> pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)]) >>> digits = datasets.load_digits() >>> X_digits […]

4. Unsupervised Learning: Seeking Representations of the Data

4. Unsupervised Lear...

4.1. Clustering: grouping observations together The problem solved in clustering Given the iris dataset, if we knew that there were 3 types of iris, but did not have access to a taxonomist to label them: we could try a clustering task: split the observations in well-separated group called clusters. 4.1.1. K-means clustering Note that there exists many […]

3. Model Selection: Choosing Estimators and Their Parameters

3. Model Selection: ...

3.1. Score, and cross-validated scores As we have seen, every estimator exposes a score method that can judge the quality of the fit (or the prediction) on new data. Bigger is better. >>> from scikits.learn import datasets, svm >>> digits = datasets.load_digits() >>> X_digits = digits.data >>> y_digits = digits.target >>> svc = svm.SVC() >>> […]

An Introduction to Natural Language Processing

An Introduction to N...

In this lecture, we will focus on text based machine learning techniques and learn how to make use of these techniques to do text classification and analysis. Natural language processing (NLP) is the study of translation of human language into something a computer can understand and manipulate. The areas of study within NLP are diverse […]

Introduction to Python

Introduction to Pyth...

I’ve been trying to learn how to program since I was ten years old. I tried many times – mostly because my dad is a developer and wanted to share the thing he loves – but Java, C, and C++ always looked scary. I couldn’t really get into it. There was too much I had […]

1. Statistical Learning: The Setting and the Estimator Object in Scikit-learn

1. Statistical Learn...

1.1. Datasets The scikit-learn deals with learning information from one or more datasets that are represented as 2D arrays. They can be understood as a list of multi-dimensional observations. We say that the first axis of these arrays is the samples axis, while the second is the features axis. A simple example shipped with the […]

Introduction to Flask as a Micro-framework

Introduction to Flas...

For those of you who are not familar with it, Flask is a web development framework written in Python. To understand how to use Flask, let’s first consider the definition of a framework. Def: Framework := A framework in coding is a set of classes, functions, and variables that form a mindset for thinking about […]