dvruette

import copy
import torch
import torch.nn as nn


class DecayToInit(nn.Module):
    def __init__(self, param: torch.Tensor):
        super().__init__()
        self.register_buffer("param", param)

ekzhang

nullcline

import traceback
import openai
import sys

# list models
models = openai.Model.list()

def baka(error, character="tsundere",):
    exc_type, exc_value, exc_traceback = sys.exc_info()
    traceback_list = traceback.extract_tb(exc_traceback)

devmotion

using AdvancedMH
using ArraysOfArrays
using CairoMakie
using DiffEqNoiseProcess
using Distributions
using StochasticDiffEq
using Turing
using Random

struct CrankNicolsonProposal{P,T} <: AdvancedMH.Proposal{P}

erincandescent

Foreward

This document was originally written several years ago. At the time I was working as an execution core verification engineer at Arm. The following points are coloured heavily by working in and around the execution cores of various processors. Apply a pinch of salt; points contain varying degrees of opinion.

It is still my opinion that RISC-V could be much better designed; though I will also say that if I was building a 32 or 64-bit CPU today I'd likely implement the architecture to benefit from the existing tooling.

Mostly based upon the RISC-V ISA spec v2.0. Some updates have been made for v2.2

Original Foreword: Some Opinion

The RISC-V ISA has pursued minimalism to a fault. There is a large emphasis on minimizing instruction count, normalizing encoding, etc. This pursuit of minimalism has resulted in false orthogonalities (such as reusing the same instruction for branches, calls and returns) and a requirement for superfluous instructions which impacts code density both in terms of size and

sbarratt

def get_jacobian(net, x, noutputs):
    x = x.squeeze()
    n = x.size()[0]
    x = x.repeat(noutputs, 1)
    x.requires_grad_(True)
    y = net(x)
    y.backward(torch.eye(noutputs))
    return x.grad.data

debasishg

1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• [Understanding

lmullen

The file page.st goes in the templates/ directory in the Gitit wiki home directory. You'll put the Ace JavaScript and CSS files in static/.

miguelfrde

Arch Linux installation (preinstalled Windows 8.1 dual boot)

Before

1. Disable Windows Fast-Startup
2. Disable Secure Boot

Partitioning

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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