tonymtz

# first:
lsbom -f -l -s -pf /var/db/receipts/org.nodejs.pkg.bom | while read f; do  sudo rm /usr/local/${f}; done
sudo rm -rf /usr/local/lib/node /usr/local/lib/node_modules /var/db/receipts/org.nodejs.*

# To recap, the best way (I've found) to completely uninstall node + npm is to do the following:

# go to /usr/local/lib and delete any node and node_modules
cd /usr/local/lib
sudo rm -rf node*

brettlangdon

Wallpapers

Install with git git clone https://gist.github.com/85942af486eb79118467.git ~/Pictures/wallpapers

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)

arno-di-loreto

Open API Specification (fka Swagger Specification) tutorial

All these Gist files are explained on my Open API Specification (fka Swagger Specification) tutorial on API Handyman blog.

The tutorial

This tutorial is composed of several posts, here are the posts links and files used for each one:

• Part 1: Introduction
  • A JSON specification
  • A YAML specification
• Part 2: The basics

cjratcliff

import tensorflow as tf

class AdamOptimizer(tf.train.Optimizer):
	def __init__(self, alpha=0.001, 
						beta1=0.9, 
						beta2=0.999,
						epsilon=1e-8):
		
		self.alpha = alpha
		self.beta1 = beta1

mauri870

After a weekend of research, stress and pain I finally figure out how to install manjaro 17 and configure the nvidia/bumblebee drivers on my avell laptop

Here's my notebook specs:

$ inxi -MGCNA

Machine:   Device: laptop System: Avell High Performance product: 1513
           Mobo: N/A model: N/A v: 0.1 UEFI: American Megatrends v: N.1.02 date: 09/28/2016
Battery    BAT0: charge: 44.0 Wh 100.0% condition: 44.0/44.0 Wh (100%)

alper111

# MIT License
# 
# Copyright (c) 2024 Alper Ahmetoglu
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

mblondel

# Authors: Mathieu Blondel, Vlad Niculae
# License: BSD 3 clause

import numpy as np


def _gen_pairs(gen, max_iter, max_inner, random_state, verbose):
    rng = np.random.RandomState(random_state)

    # if tuple, interpret as randn

Kulbear

import torch
import tensorflow as tf

import time
import numpy as np

def gather_nd_torch(params, indices, batch_dim=1):
    """ A PyTorch porting of tensorflow.gather_nd
    This implementation can handle leading batch dimensions in params, see below for detailed explanation.

Kulbear

import tensorflow as tf
import torch
import numpy as np

def gather_nd_torch(params, indices, batch_dim=1):
    """ A PyTorch porting of tensorflow.gather_nd
    This implementation can handle leading batch dimensions in params, see below for detailed explanation.

    The majority of this implementation is from Michael Jungo @ https://stackoverflow.com/a/61810047/6670143
    I just ported it compatible to leading batch dimension.