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@veekaybee
veekaybee / normcore-llm.md
Last active June 14, 2024 07:51
Normcore LLM Reads

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Screenshot 2023-12-18 at 10 40 27 PM

Pre-Transformer Models

@russmo
russmo / docker-run-crashplan-pro.gist
Last active February 28, 2019 20:15
Docker run command for jlesage/crashplan-pro on QNAP NAS with CrashPlanPro version 6.6+. Version 6.6 introduces a new user interface and "officially" drops "unofficial" support for headless installations on servers and NAS devices. This docker build implements a handy VNC-in-browser to allow this to run on a NAS. This is what I ran to launch it …
docker run -d \
--name=crashplan-pro \
-h $HOSTNAME \
-e USER_ID=0 \
-e GROUP_ID=0 \
-e TZ=“America/Los_Angeles” \
-p 5800:5800 \
-p 5900:5900 \
-v /share/CACHEDEV1_DATA/Container/config/crashplanpro:/config:rw \
-v /share/CACHEDEV1_DATA:/storage:rw \
# git clone from https://github.com/tkarras/progressive_growing_of_gans
# download the snapshot from their Google drive
# use the following code in the same directory to generate random faces
import os
import sys
import time
import glob
import shutil
import operator
import theano
@victor-shepardson
victor-shepardson / pytorch-glumpy.md
Last active September 10, 2022 16:09
using pycuda and glumpy to draw pytorch GPU tensors to the screen without copying to host memory
@brannondorsey
brannondorsey / pix2pix_paper_notes.md
Last active January 3, 2022 09:57
Notes on the Pix2Pix (pixel-level image-to-image translation) Arxiv paper

Image-to-Image Translation with Conditional Adversarial Networks

Notes from arXiv:1611.07004v1 [cs.CV] 21 Nov 2016

  • Euclidean distance between predicted and ground truth pixels is not a good method of judging similarity because it yields blurry images.
  • GANs learn a loss function rather than using an existing one.
  • GANs learn a loss that tries to classify if the output image is real or fake, while simultaneously training a generative model to minimize this loss.
  • Conditional GANs (cGANs) learn a mapping from observed image x and random noise vector z to y: y = f(x, z)
  • The generator G is trained to produce outputs that cannot be distinguished from "real" images by an adversarially trained discrimintor, D which is trained to do as well as possible at detecting the generator's "fakes".
  • The discriminator D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator.
  • Unlike an unconditional GAN, both th
@EderSantana
EderSantana / CATCH_Keras_RL.md
Last active October 16, 2023 08:32
Keras plays catch - a single file Reinforcement Learning example
@oglops
oglops / yaml_OrderedDict.py
Last active January 25, 2023 11:08
write to and load from yaml file with OrderedDict
#!/usr/bin/env python
try:
# for python newer than 2.7
from collections import OrderedDict
except ImportError:
# use backport from pypi
from ordereddict import OrderedDict
import yaml
@matsen
matsen / md-nb-diffs.md
Last active February 22, 2021 21:44 — forked from iamlemec/nb2md
Markdown diffs for jupyter notebooks.

For sane jupyter notebook diffs

  • Install the nbconvert package, though you probably already have it if you are using jupyter.
  • Put the nb2md script below in your path and make executable
  • Add the following to your .gitattributes file, which can be in your home directory (use nb2md for all projects) or in the root of your project:
*.ipynb diff=nb2md
  • Run
@baraldilorenzo
baraldilorenzo / readme.md
Last active June 13, 2024 03:07
VGG-16 pre-trained model for Keras

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

@karpathy
karpathy / min-char-rnn.py
Last active June 16, 2024 04:05
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np
# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)