jaseemabid

A bunch of good git tutorials

Intro to git

• Simplest intro to git by github and codeschool - Try Git

• PeepCode Git Internals PDF

• [Intro to github]

miguelgrinberg

#!flask/bin/python
from flask import Flask, jsonify, abort, request, make_response, url_for
from flask_httpauth import HTTPBasicAuth

app = Flask(__name__, static_url_path = "")
auth = HTTPBasicAuth()

@auth.get_password
def get_password(username):
    if username == 'miguel':

karpathy

"""
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)

krishvishal

from model import Net
from trainer import Trainer 
import torch
from torch import nn
from matplotlib import pyplot as plt

model = Net()
ckpt = torch.load('path_to_checkpoint')
model.load_state_dict(ckpt['state_dict'])
filter = model.conv1.weight.data.numpy()

cedrickchee

# Step - Submit Predictions
# We have finished training and ready to run predictions on the test set.
log_test_preds = learn.predict(is_test=True)

# Convert log predictions to just probabilities (predictions).
test_preds = np.exp(log_test_preds)

# Create the submission file using the probabilities
# Get a list of image file names from the test data loader
im_fnames = data.test_dl.dataset.fnames

sloanlance

jq: JSONL ↔︎ JSON conversion

Prerequisites

• jq — https://jqlang.org/ — "like sed for JSON data"

  There are several options available for installing jq. I prefer to use Homebrew: brew install jq

• JSONL → JSON

raulqf

Install OpenCV 4.10 with CUDA 12.6 and CUDNN 8.9 in Ubuntu 24.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

mlblogger

import gym
from gym.envs.registration import register
from gym import error, spaces, utils
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader.data as web
import arrow
import random
import sys