saratrajput

Mujoco and Mujoco-py Installation Instructions

The steps are taken from this video and document with some small changes.

Steps

1. Install Anaconda. Download it from this link.

cd Downloads/
sudo chmod +x Anaconda3-2021.11-Linux-x86_64.sh
./Anaconda3-2021.11-Linux-x86_64.sh

jeffshee

# Resources
https://phoenixnap.com/kb/install-ubuntu-20-04
https://rufus.ie

# Steps
0. In Windows, shrink the disk partition to create free space for dual boot.
(Refer: https://www.linuxtechi.com/dual-boot-ubuntu-20-04-lts-along-with-windows-10/)
1. Restart. In BIOS, disable Secure Boot.
2. (Optional) Check if Windows boot successfully after Secure Boot is disabled. (It should!)
3. Restart. Boot the Ubuntu installer by either changing or overridding the boot order in BIOS.

Guitaricet

Reproducibility

ML experiments may be very hard to reproduce. You have a lot of hyperparameters, different dataset splits, different ways to preprocess your data, bugs, etc. Ideally, you should log data split (already preprocessed), all hyperparameters (including learning rate scheduling), the initial state of your model and optimizer, random seeds used for initialization, dataset shuffling and all of your code. Your GPU is also should be in deterministic mode (which is not the default mode). For every single model run. This is a very hard task. Different random seed can significantly change your metrics and even GPU-induced randomness can be important. We're not solving all of these problems, but we need to address at least what we can handle.

For every result you report in the paper you need (at least) to:

1. Track your model and optimizer hyperparameters (including learning rate schedule)
2. Save final model parameters
3. Report all of the parameters in the pap

Rowing0914

import numpy as np
from autograd import grad
import matplotlib.pyplot as plt


def f(x):
    return x ** 3

def f_first(x):
    return 3 * x ** 2

zigmoo

Screen Quick Reference

Wait a minute, why would anyone use 'screen', and what is it anyway?

Well, because it's both AWESOME and FUN!!

It lets you keep your own session running on all the servers you already use, and chances are, it's probably already installed and waiting for you! (since 1987!)

Basic

| Description | Command |

shreydesai

import torch
import torch.nn as nn
import numpy as np

class DotProductAttention(nn.Module):
    
    def __init__(self, query_dim, key_dim, value_dim):
        super().__init__()
        
        self.scale = 1.0/np.sqrt(query_dim)

kyamagu

gustavohenrique

import sys
import base64
from Crypto.Cipher import AES


class AESCipher(object):
    def __init__(self, key):
        self.bs = 16
        self.cipher = AES.new(key, AES.MODE_ECB)

zakkak

# [<tag>] (If applied, this commit will...) <subject> (Max 72 char)
# |<----   Preferably using up to 50 chars   --->|<------------------->|
# Example:
# [feat] Implement automated commit messages

# (Optional) Explain why this change is being made
# |<----   Try To Limit Each Line to a Maximum Of 72 Characters   ---->|

# (Optional) Provide links or keys to any relevant tickets, articles or other resources
# Example: Github issue #23

namoshizun

"""
Implement a very simple gaussian process for regression task.
Credit : http://katbailey.github.io/post/gaussian-processes-for-dummies/
"""

import numpy as np
import matplotlib.pyplot as pl


def prepare_data(n, fn):