b01

#!/bin/sh

# Copyright 2023 Khalifah K. Shabazz
#
# 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:

zthxxx

Note

For every body who using Win10/11 with new Office that you need to know:

to active Office without crack, just follow https://github.com/WindowsAddict/IDM-Activation-Script,

you wiil only need to run

irm https://massgrave.dev/ias | iex

liangfu

#!/usr/bin/python

# Copyright 2015-2016 Nervana Systems Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#

Jamesits

#!/bin/bash
set -eu

SYSCTL_FILE=/etc/sysctl.d/90-tcp-bbr.conf

# check root
if [[ $EUID -ne 0 ]]; then
   echo "This script must be run as root" 
   exit 1
fi

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

karpathy

--[[ 
Efficient LSTM in Torch using nngraph library. This code was optimized 
by Justin Johnson (@jcjohnson) based on the trick of batching up the 
LSTM GEMMs, as also seen in my efficient Python LSTM gist.
--]]

function LSTM.fast_lstm(input_size, rnn_size)
  local x = nn.Identity()()
  local prev_c = nn.Identity()()
  local prev_h = nn.Identity()()

adam-p

A rather dirty way to patch module code at runtime.

jdeng

  // generate [0..n-1]
  auto seq = [](size_t n) -> std::vector<size_t> {
      std::vector<size_t> v(n);
      for (size_t i=0; i<n; ++i) v[i] = i;
      return v;
  };
  auto index = seq(n);

  // n * n distance matrix
  std::vector<D> dists(n * n);

onauparc

#include <cuda_runtime.h>

#include <cstring>
#include <cstdlib>
#include <vector>

#include <string>
#include <iostream>
#include <stdio.h>
#include "caffe/caffe.hpp"

kevin-smets

Default

Powerlevel10k