Skip to main content

Tutorials on Deep-Learning: from Supervised to Unsupervised Learning

These tutorials were designed for the IPAM Summer School on Deep Learning: more info here..
This directory contains four tutorials, which intend to teach how to train deep-learning models using supervised and unsupervised techniques, using Torch7.
The Torch tutorials are less interactive than the IPython ones. Rather, they give you complete end-to-end programs, which you can use as a solid starting point to develop your own programs/scripts.
These tutorials should be read/done in order.
This text can be browsed either from the html files, or directly on GitHub, by navigating through the directory structure.
If you're reading this on GitHub, you won't see the Math properly rendered, please read the tutorials here.
HTML Links (if you're browsing an HTML version of this help, i.e. not GitHub):
GitHub Links:

Comments

Popular posts from this blog

Introduction to Machine Learning in Python

Python tutorials for introduction to machine learning Introduction to Machine Learning in Python This repository provides instructional material for machine learning in python. The material is used for two classes taught at NYU Tandon by  Sundeep Rangan : EE-UY / CS-UY 4563: Introduction to Machine Learning (Undergraduate) EL-GY 6123: Introduction to Machine Learning (Graduate) Anyone is free to use and copy this material (at their own risk!). But, please cite the material if you use the material in your own class. Pre-requisites All the software can be run on any laptop (Windows, MAC or UNIX).  Instructions  are also provided to run the code in Google Cloud Platform on a virtual machine (VM). Both classes assume no python or ML experience. However, experience with some programming language (preferably object-oriented) is required. To follow all the mathematical details and to complete the homework exercises, the class assumes undergraduate probability, ...

Python Machine Learning Notebooks (Tutorial style)

Python Machine Learning Notebooks (Tutorial style) Dr. Tirthajyoti Sarkar, Sunnyvale, CA ( You can connect with me on LinkedIn here ) Essential codes/demo IPython notebooks for jump-starting machine learning/data science. You can start with this article that I wrote in Heartbeat magazine (on Medium platform): "Some Essential Hacks and Tricks for Machine Learning with Python" Essential tutorial-type notebooks on Pandas and Numpy Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, matplotlib etc. Basics of Numpy array Basics of Pandas DataFrame Basics of Matplotlib and Descriptive Statistics Tutorial-type notebooks covering regression, classification, clustering, dimensionality reduction, and some basic neural network algorithms Regression Simple linear regression with t-statistic generation Multiple ways to do linear regression in Python and their speed comparison ( check the article I wr...

R tutorials for Data Science, NLP and Machine Learning

R Data Science Tutorials This repo contains a curated list of R tutorials and packages for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Curated list of Python tutorials for Data Science, NLP and Machine Learning . Comprehensive topic-wise list of Machine Learning and Deep Learning tutorials, codes, articles and other resources . Learning R Online Courses tryR on Codeschool Introduction to R for Data Science - Microsoft | edX Introduction to R on DataCamp Data Analysis with R Free resources for learning R R for Data Science - Hadley Wickham Advanced R - Hadley Wickham swirl: Learn R, in R Data Analysis and Visualization Using R MANY R PROGRAMMING TUTORIALS A Handbook of Statistical Analyses Using R , Find Other Chapters Cookbook for R Learning R in 7 simple steps More Resources Awesome-R Repository on GitHub R Reference Card: Cheatsheet R bloggers: blog aggregator R Resources...