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Name: Sangram Gaikwad
Type: User
Name: Sangram Gaikwad
Type: User
Official content for the Fall 2014 Harvard CS109 Data Science course
Official content for Harvard CS109
Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
Evaluation Tool for the ICDAR 2019 Competition on Table Detection and Recognition
Powerful and efficient Computer Vision Annotation Tool (CVAT)
Programming with Python for Data Science Microsoft
Deep Learning with PyTorch, published by Packt
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.
MIT Deep Learning Book in PDF format
Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)
Roadmap to becoming a developer in 2022
Learning Python Django Framework.
DjangoCon USA 2017 talk
Automatically exported from code.google.com/p/dns-server
Secure Hadoop docker image
A kerberos KDC and a kerberos client in docker containers.
Kaggle Python docker image
Apache spark docker containers (standalone mode)
Apache Spark docker image
Ready-to-run Docker images containing Jupyter applications
Spawns JupyterHub single user servers in Docker containers
Compilation of resources and insights that helped me on my journey to data scientist
Schema and utilities for Google Dataset Publishing Language
Use Jupyter Notebooks to demonstrate how to build a Recommender with Apache Spark & Elasticsearch
Distributed Deep learning with Keras & Spark
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.