CornellDataScience/Deep-Learning-Course

Name: Deep-Learning-Course

Owner: Cornell Data Science

Description: Course material for introduction to deep learning in TensorFlow

Created: 2017-08-16 23:54:07.0

Updated: 2018-03-21 11:27:36.0

Pushed: 2018-03-19 01:19:52.0

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Size: 289461

Language: Jupyter Notebook

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README

Introduction to Deep Learning in TensorFlow

This is the repository for the Deep Dive: Introduction to Deep Learning in TensorFlow, organized by Cornell Data Science.
No prior knowledge in machine learning or Python is assumed, except for simple matrix algebra and basic understanding of coding.

Installation

To download all course material, type the following into the command-line:

clone https://github.com/CornellDataScience/Deep-Learning-Course.git

For those with native pip, use the following to install all dependencies:

install -r requirements.txt

All material is written in Python 3 (although most code is Python 2-compatible).
For detailed installation instructions on git, Python, and TensorFlow, see the installation section.

Tutorials

1. Linear Classifiers
2. Fully Connected Neural Networks
3. Convolutional Neural Networks
4. Recurrent Neural Networks

About

Authors: Yuji Akimoto (CS '19), Ryan Butler (CS '19)

Course material is inspired by the CS231n course at Stanford University and the tutorials by Hvass-Labs.
All material is distributed under the MIT License.