Deep Learning Community
Inspired by the collaborative culture of artificial neural network research, we hold regular, enhanced study sessions in Vadodara. At these meetings, we summarise prescribed preparatory material and leverage our individual strengths in computer science, mathematics, statistics, neuroscience, and venture capital to cement our comprehension of concepts and to implement effective deep learning models.
Over the course of our sessions, we follow three parallel paths:
- Theory: We study academic textbooks, exercises, and coursework so that we command strong theoretical foundations for neural networks and deep learning. Broadly, we cover calculus, algebra, probability, computer science, with a focus on their intersection at machine learning.
- Application: We practice deep learning in the real world. We typically commence by collectively following tutorials then we move on to solving novel and illustrative data problems involving a broad range of techniques. In addition to incorporating deep learning into our respective academic and commercial applications, we commit code to the present, public repository where possible.
- Presentations: Study group members regularly share their progress on Deep Learning projects and their area of expertise. This elicits novel discourse outside of the relatively formal paths 1 and 2, playfully encouraging along serendipity.
- Fei-Fei Li, Andrej Karpathy and Justin Johnson's CS231n on Convolutional Neural Networks for Visual Recognition (sessions VI through VIII)
- Richard Socher and Christopher Manning's CS224D (2016)/CS224N (2017) on Deep Learning for Natural Language Processing (sessions IX through XIII)
- Reinforcement Learning (session XIV)
If you're looking to get a handle on the fundamentals of Deep Learning, enroll in his 30-hour, in-classroom course at the NYC Data Science Academy.
Our applications have involved a broad range of neural network architectures built largely in Python with Numpy, TensorFlow, and/or Keras.
In chronological order, we have experienced the joy of being enlightened by:
Click through for detailed summary notes from each session:
- Directory of fastai and DL terms
- Solving the Most Common Errors
- Fastai FAQs for Beginners
- 🔴 Resources (Blogs Written by fastai Fellows / Research Papers, etc)
- Paperspace setup
- StreamInterrupt Docker Container
- Paperspace Docker Container
- Set up personal deep learning box (home computer)
- manual: bash script for setup
- the CUDA drivers
- Anaconda (special Python distribution)
- Python libraries
- fastai library
- courses
- data
- Other
- tmux on AWS
- Download data using Kaggle CLI
- Download data using Chrome and wget
- Jupyter Notebook Commands & Shortcuts
- How to Create a Keypair
- Copy Files from Local PC to Cloud PC