Jayesh K. Gupta rejuvyesh

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

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

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

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/.

Arch Linux installation (preinstalled Windows 8.1 dual boot)

Before

Disable Windows Fast-Startup
Disable Secure Boot

Partitioning

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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.

	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)

	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)

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

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

	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