mallamanis

[
    "https://github.com/minimaxir/big-list-of-naughty-strings.git",
    "https://github.com/shadowsocks/shadowsocks.git",
    "https://github.com/littlecodersh/ItChat.git",
    "https://github.com/google-research/bert.git",
    "https://github.com/0voice/interview_internal_reference.git",
    "https://github.com/keon/algorithms.git",
    "https://github.com/satwikkansal/wtfpython.git",
    "https://github.com/drduh/macOS-Security-and-Privacy-Guide.git",
    "https://github.com/google/python-fire.git",

jessvb

Teaching Tech to Talk: K-12 Conversational Artificial Intelligence Literacy Curriculum and Development Tools

This Gist contains additional information about the studies presented in "Teaching Tech to Talk: K-12 Conversational Artificial Intelligence Literacy Curriculum and Development Tools" at EAAI 2021 (focused on learning outcomes) and "'Alexa, Can I Program You?': Student Perceptions of Conversational Artificial Intelligence Before and After Programming Alexa" at IDC 2021 (focused on student perceptions of Alexa). There is also a video demo of the work.

Contents:

• Appendix A contains code snippets from the "cookbook" conversational AI skill developed in MIT App Inventor.
• [Appendix B](https://gist.github.com/jessvb/1cd959e32415a6ad4389761c49b54bbf#appendix-b-workshop-curriculum---activities-and-p

thomwolf

import os; import psutil; import timeit
from datasets import load_dataset

mem_before = psutil.Process(os.getpid()).memory_info().rss >> 20
wiki = load_dataset("wikipedia", "20200501.en", split='train')
mem_after = psutil.Process(os.getpid()).memory_info().rss >> 20
print(f"RAM memory used: {(mem_after - mem_before)} MB")

s = """batch_size = 1000
for i in range(0, len(wiki), batch_size):

bkocis

Deep Reinforcement Learning ND:

• Navigation (Value based methods)
• Continuous Control (Policy based methods)
• Collaboration and competition (Multi-Agent Reinforcement Learning)

erogol

import torch as th                                                                 
                                                                                   
                                                                                   
class NLL_OHEM(th.nn.NLLLoss):                                                     
    """ Online hard example mining. 
    Needs input from nn.LogSotmax() """                                             
                                                                                   
    def __init__(self, ratio):      
        super(NLL_OHEM, self).__init__(None, True)                                 
        self.ratio = ratio