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This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

tensorflow machine-learning regression convolutional-neural-networks logistic-regression book reinforcement-learning autoencoder linear-regression classification clusteringThis GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

machine-learning machine-learning-algorithms logistic-regression data-science data-mining scikit-learn neural-networkCourse materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15).

data-science machine-learning scikit-learn data-analysis pandas jupyter-notebook course linear-regression logistic-regression model-evaluation naive-bayes natural-language-processing decision-trees ensemble-learning clustering regular-expressions web-scraping data-visualization data-cleaningThis repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3 neural-network k-nearest-neighbours k-nearest-neighbor k-nn neural-networksPython codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithmThis is the code that accompanies my blog post Getting started with TensorFlow on iOS. It uses TensorFlow to train a basic binary classifier on the Gender Recognition by Voice and Speech Analysis dataset.

tensorflow metal ios machine-learning logistic-regression voice-recognitionYtk-learn is a distributed machine learning library which implements most of popular machine learning algorithms

machine-learning distributed gbm gbdt logistic-regression factorization-machines spark hadoopRead the full article. This project trains a computer program to identify a voice as male or female, based upon acoustic properties of the voice and speech. The model is trained on a dataset consisting of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R and then processed with artificial intelligence/machine learning algorithms to learn gender-specific traits for classifying the voice as male or female.

gender-recognition gender machine-learning data-science artificial-intelligence neural-network logistic-regression vocal voice speech acoustic-properties signal aiA set of machine learning experiments in Clojure

logistic-regression regularization linear-regressionIt's a logistic regression demo showed first time at my personal blog linked in description area. Basically, it's a python program for command line that download and process some news content from brasilian sites and use scikit-learn Logistic Regression to classify them in some categories.

machine-learning logistic-regressionThis is a distributed logistic regression system based on ps-lite, which is a distributed parameter server.

logistic-regression ps-liteThis is a library for regression analysis of data. That is, it attempts to find the line of best fit to describe a relationship within the data. It takes in a series of training observations, each consisting of features and an outcome, and finds how much each feature contributes to the outcome. This library also handles logistic regression, in which the outcomes are booleans. In this case, the regression would give you how much each feature contributes to the probability of the outcome and the prediction process would give you the probability of the outcome for a given new example.

logistic-regression gradient-descent multiple regression#Update I've made some update on the data loading logic so now it reads in csv-format file. Previous version is still accessible but it's no longer supported. #Introduction Javascript implementation of several machine learning algorithms including Decision Tree and Logistic Regression this far. More to come.

logistic-regression decision-tree machine-learning classifier c4.5Load the package (it, of course, plays nicely with tidyverse). Conduct an Ordinary Least Squares (OLS) regression analysis.

tidyverse tidy rstats r statistics analysis academic research science mkearney-r-package regression linear-models general-linear-model logistic-regression poisson-regression negative-binomial-regression robust-regression structural-equation-modeling latent-variables tidyversityR package which implements Follow the proximally-regularized leader algorithm. It allows to solve very large problems with stochastic gradient descend online learning. See Ad Click Prediction: a View from the Trenches for example.

logistic-regression sgd ftrl r machine-learningThe GDLibrary is a pure-Matlab library of a collection of unconstrained optimization algorithms. This solves an unconstrained minimization problem of the form, min f(x). Note that the SGDLibrary internally contains this GDLibrary.

optimization optimization-algorithms machine-learning machine-learning-algorithms big-data gradient-descent gradient logistic-regression newton linear-regression svm lasso matrix-completion rosenbrock-problem softmax-regression multinomial-regression statistical-learning classificationThe SGDLibrary is a pure-MATLAB library of a collection of stochastic optimization algorithms. This solves an unconstrained minimization problem of the form, min f(x) = sum_i f_i(x). The SGDLibrary is also operable on GNU Octave (Free software compatible with many MATLAB scripts). Note that this SGDLibrary internally contains the GDLibrary.

optimization optimization-algorithms machine-learning machine-learning-algorithms stochastic-optimization-algorithms stochastic-gradient-descent big-data gradient-descent-algorithm gradient logistic-regression sgd variance-reduction newtons-method linear-regression classification online-learning quasi-newtonThe SparseGDLibrary is a pure-Matlab library of a collection of unconstrained optimization algorithms for sparse modeling. Run run_me_first for path configurations.

optimization optimization-algorithms machine-learning-algorithms machine-learning big-data gradient-descent sparse-linear-solver sparse-regression lasso-regression lasso elasticnet solver algorithms admm proximal-algorithms proximal-operators logistic-regression matrix-completion coordinate-descent support-vector-machines:racehorse: Introduction to TensorFlow :snowflake:

tensorflow machine-learning hello-world logistic-regression ai 101
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