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agile_data_code_2

Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

ai

My blog

attention-model

awesome-piracy

A curated list of awesome warez and piracy links

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

coding-interview-university

A complete computer science study plan to become a software engineer.

d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

data-science-ipython-notebooks

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

data-science-primer

A set of self paced resources for anyone looking to get into data science. The materials assume an absolute beginner and are intended to prepare students for the Galvanize Data Science interview process: http://www.galvanize.com/courses/data-science/

data-structures-and-algorithms-in-python

Exercise solutions for Data Structures and Algorithms in Python(Michael T. Goodrich)

deepart---neural-network-that-learns-van-gogh-s-art

Uses a pre-trained model - VGG16 by Oxford's Visual Geometry Group

devops-exercises

Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization

dialogpt

Large-scale pretraining for dialogue

dl4nlp

Deep Learning for NLP resources

early-childhood-longitudinal-study

epijudge

EPI Judge - Preview Release

ganhacks

starter from "How to Train a GAN?" at NIPS2016

generate-new-us-city-names-using-lstm

gensim

Topic Modelling for Humans

keras-idiomatic-programmer

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

lda2vec

lda2vec-featurizer

lda2vec-tf

tensorflow port of the lda2vec model for unsupervised learning of document + topic + word embeddings

machine-learning-tutorials

machine learning and deep learning tutorials, articles and other resources

marketing-analysis-using-markov-chain-models

Statistical vs Managerial Segmentstion

models

Models built with TensorFlow

python_ds

data structures and algorithms

relevance-ranking-using-latent-semantic-indexing--from-scratch-

Latent Semantic Analysis Introduction: An information retrieval technique patented in 1988. In the context of its application to information retrieval, it is sometimes called Latent Semantic Indexing (LSI). LSI allows a search engine to determine what a page is about outside of specifically matching search query text. It looks at “Themes” instead of “Keywords”. Linear Algebra techniques used in the project: Singular Value Decomposition, Cosine Similarity, Matrix properties. Dataset: “Sci.space” news group from 20 news groups dataset, available in the Scikit-Learn library. It contains 400 news articles related to space. SVD (Singular Value Decomposition): SVD is a matrix decomposition algorithm, it decomposes a matrix into 3 matrices which are a set to transformations. Decomposition leads to an orthogonal matrix U, Diagonal matrix S and a Diagonal Matrix V. This is the best possible transformation of a matrix. In this decomposition method we are looking for a set of orthonormal basis in the row space that when multiplied by the original matrix goes to an orthonormal basis in the column space.Av1 = σ1u1  Av2 = σ2u2 

scala-style-guide

Databricks Scala Coding Style Guide

spark-install

Installation guide for Apache Spark + Hadoop on Mac/Linux