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Machine learning for hackers


machine learning for hackers

Ultimately in predictive modeling machine learning projects you are laser focused on making accurate predictions rather than interpreting the results.
Mne-python-notebooks - IPython notebooks for EEG/MEG data processing using mne-python Neon Course - IPython notebooks for a complete course around understanding Nervana's Neon pandas cookbook - Recipes for using Python's pandas library climin - Optimization library focused on machine learning, pythonic implementations of gradient descent.
DA - Julia package for Regularized Discriminant Analysis Regression - Algorithms for regression analysis (e.g.Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.Libra-Tk - Algorithms for learning and inference with discrete probabilistic models.PyStanfordDependencies - Python interface for converting Penn Treebank trees to Stanford Dependencies.Google still saves plenty of goodies for its own programmers.This project is under the Apache 2 license.Built out of the Google Brain project, and co-invented by Dean and his colleague Rajat Monga, TensorFlow helped democratize machine learning by standardizing the often tedious and esoteric details involved in building a system especially since Google made it available to the public in November.(The joke misses the point that Google does need them.) My students, no matter who, always get an offer from Google.WindML - A Python Framework for Wind Energy Analysis and Prediction vispy - GPU-based high-performance interactive OpenGL 2D/3D data visualization library cerebro2 A web-based visualization and debugging platform for NuPIC.Optunity examples - Examples demonstrating how to use Optunity in synergy with machine learning libraries.Encog-cpp Fido - A highly-modular C machine learning library for embedded electronics and robotics.Here is a quick collection of such books to start your fair weather study off on the right foot.



In fact, over a decade ago, Google was running in-house courses to teach its engineers machine learning.
Smart Reply is only one data point in a dense graph of instances where ML has proved effective at Google.
Logistic Regression Resources Checkout some of the books below for more details on the logistic regression algorithm.
Group-lasso - Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model jProcessing - Kanji / Hiragana / Katakana getdata recover my files pro 5.2.1 to Romaji Converter.
It does assume a linear relationship between the input variables with the output.MLKit - A simple Machine Learning Framework written in Swift.Because they are more likely to pick up the phone.Ruffus - Computation Pipeline library for python.At software maker Adobe Systems, CIO Cynthia Stoddard is reimagining her department with a data-driven operating model, relying on Hadoop-based analytics to gain insights to both run IT and the business better.Skll - A wrapper around scikit-learn that makes it simpler to conduct experiments.Unsup - A package for unsupervised learning in Torch.Cuda - This is a fast C/cuda implementation of convolutional deep learning cxxnet - Yet another deep learning framework with less than 1000 lines core code deep learning DeepDetect - A machine learning API and server written in C11.MLlib in Apache Spark cod penal 2014 pdf - Distributed machine learning library in Spark Hydrosphere Mist - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.Rendering beautiful SVG maps in Python.




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