All Projects → lcdm-uiuc → Info490 Fa16

lcdm-uiuc / Info490 Fa16

Licence: other
INFO 490: Foundations of Data Science, offered in the Fall 2016 Semester at the University of Illinois

Projects that are alternatives of or similar to Info490 Fa16

365datascience
This Repo Contains all the exercise files for Data Science Course of 365 Datascience . The repo is split into the relevant folders & there is one exercise folder which contains all the files of that course. Don't forget to star it :D
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Keras2kubernetes
Open source project to deploy Keras Deep Learning models packaged as Docker containers on Kubernetes.
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Commitgen
Code and data for the paper "A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes"
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Transformer Tts
Implementation of "FastSpeech: Fast, Robust and Controllable Text to Speech"
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
25daysinmachinelearning
I will update this repository to learn Machine learning with python with statistics content and materials
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Notebooks
Some notebooks
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Figure Gen
A Python package to effortlessly assemble images in comparison figures. Supports LaTeX, PPTX, and HTML.
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Info490 Sp17
Advanced Data Science, University of Illinois Spring 2017
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Visualizing And Understanding Convolutional Neural Networks
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Stock Market Prediction Using Natural Language Processing
We used Machine learning techniques to evaluate past data pertaining to the stock market and world affairs of the corresponding time period, in order to make predictions in stock trends. We built a model that will be able to buy and sell stock based on profitable prediction, without any human interactions. The model uses Natural Language Processing (NLP) to make smart “decisions” based on current affairs, article, etc. With NLP and the basic rule of probability, our goal is to increases the accuracy of the stock predictions.
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Gan
python notebooks accompanying the book Make Your Own GAN
Stars: ✭ 50 (-5.66%)
Mutual labels:  jupyter-notebook
Policy Gradient Methods
Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC
Stars: ✭ 54 (+1.89%)
Mutual labels:  jupyter-notebook
Homeless Arrests Analysis
A Los Angeles Times analysis of arrests of the homeless by the LAPD
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Aistudio Searching Data Dumps With Use
searching large heterogenous data dumps with Universal Sentence Encoder
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Wiki generator live
live code
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Neural Process Family
Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Data Privacy For Data Scientists
A workshop on data privacy methods for data scientists.
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Tutoriais De Am
Algoritmos de aprendizado de máquina criados manualmente para maior compreensão das suas funcionalidades
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Mypresentations
this is my presentaion area .个人演讲稿展示区,主要展示一些平时的个人演讲稿或者心得之类的,
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook
Handwritten Character Recognition
This a Deep learning AI system which recognize handwritten characters, Here I use chars74k data-set for training the model
Stars: ✭ 53 (+0%)
Mutual labels:  jupyter-notebook

Welcome to INFO 490: Foundations of Data Science

Join the chat at https://gitter.im/UI-DataScience/info490-fa16

Professor: Dr. Robert Brunner

Course Administrator Edward Kim (Ph.D. candidate Physics)

Teaching Assistants:

  1. Taeyoung Kim (Statistics + CS undergraduate)
  2. Xinyang Lu (Ph.D. candidate Astronomy)
  3. Andrew Mehrmann (MS candidate Statistics)
  4. Samantha Thrush (Ph.D. candidate Astronomy)

For contact information see the course moodle site

This class is an asynchronous, online course. This course will build a practical foundation for data science by teaching students basic tools and techniques that can scale to large computational systems and massive data sets.

Students will first learn how to work at a Unix command prompt before learning about source code control software like git and the github site. Next, the Python programming language will be covered, with a focus on specific aspects of the language and associated Python modules that are relevant for Data Science. Python will be introduced and used primarily via the IPython (or Jupyter) Notebooks, and will cover the Numpy, Scipy, MatPlotlib, Pandas, Seaborn, and scikit_learn Python modules. These capabilities will be demonstrated through simple data science tasks such as obtaining data, cleaning data, visualizing data, and basic data analysis. Students must have access to a fairly modern computer, ideally that supports hardware virtualization, on which they can install software.

This class is open to sophomores, juniors, seniors and graduate students in any discipline.

Please refer to the course syllabus for more information about course content and grading policies.

If you have any questions, or if something is not working properly, PLEASE look through the FAQs wiki page (please look at the right tool bar on the Github course page and click the icon labeled "Wiki" that looks like an open book) and the Moodle Q&A Forum before emailing TA or course instructor.

Click the link below to get live help on Gitter:

Join the chat at https://gitter.im/UI-DataScience/info490-fa16

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].