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First Steps of Learning Deep Learning: Image Classification in Keras
Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!
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Google Analytics Audit Checklist and Tools
In this post, a Google Analytics & Google AdWords expert shares his tips and tools of intelligent Google Analytics auditing. Read on for some practical insight.
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Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings
This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.
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The Machine Learning Abstracts: Decision Trees
Decision trees are a classic machine learning technique. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree.
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Visualizing Convolutional Neural Networks with Open-source Picasso
Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?
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The Machine Learning Abstracts: Classification
Classification is the process of categorizing or “classifying” some items into a predefined set of categories or “classes”. It is exactly the same even when a machine does so. Let’s dive a little deeper.
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 Machine Learning Exercises in Python: An Introductory Tutorial Series
This post presents a summary of a series of tutorials covering the exercises from Andrew Ng's machine learning class on Coursera. Instead of implementing the exercises in Octave, the author has opted to do so in Python, and provide commentary along the way.
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The Truth About Bayesian Priors and Overfitting
Many of the considerations we will run through will be directly applicable to your everyday life of applying Bayesian methods to your specific domain.
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How to Build a Data Science Pipeline
Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.
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Exploratory Data Analysis in Python
We view EDA very much like a tree: there is a basic series of steps you perform every time you perform EDA (the main trunk of the tree) but at each step, observations will lead you down other avenues (branches) of exploration by raising questions you want to answer or hypotheses you want to test.
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Machine Learning Exercises in Python: An Introductory Tutorial Series