bigsnarfdude

https://web.stanford.edu/class/cs25/

https://www.youtube.com/watch?v=mcep6W8oB1I https://www.youtube.com/watch?v=fz8wf9hN20c https://www.youtube.com/watch?v=wwQ1LQA3RCU https://www.youtube.com/watch?v=1GbDTTK3aR4 https://www.youtube.com/watch?v=ylEk1TE1uBo https://www.youtube.com/watch?v=mE7IDf2SmJg https://www.youtube.com/watch?v=ckNMsUuLryM

bigsnarfdude

Hardware Requirement

Method	Bits	7B	13B	30B	65B	8x7B
Full	16	160GB	320GB	600GB	1200GB	900GB
Freeze	16	20GB	40GB	120GB	240GB	200GB
LoRA	16	16GB	32GB	80GB	160GB	120GB
QLoRA	8	10GB	16GB	40GB	80GB	80GB
QLoRA	4	6GB	12GB	24GB	48GB	32GB

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t