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Motivation: Deep learning in vision, speech, text, robotics.
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Deep learning courses:
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from typing import ( | |
List, | |
TypeVar, | |
) | |
# Type aliases | |
A = TypeVar('A') | |
Digit = int | |
Matrix = List[List[A]] | |
Grid = Matrix[Digit] |
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{-# LANGUAGE TypeOperators #-} | |
{-# LANGUAGE GeneralizedNewtypeDeriving #-} | |
import Control.Applicative | |
import Data.Char | |
import Data.List (intersperse) | |
import Data.Monoid hiding (All, Any) | |
import Data.Foldable hiding (all, any) | |
import Prelude hiding (all, any) |
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from functools import singledispatch | |
class Document: | |
pass | |
class Paragraph(Document): | |
def __init__(self, text): | |
self.text = text |
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import argparse | |
import pdb | |
import os | |
import sys | |
from collections import namedtuple | |
import numpy as np | |
import pandas as pd |
Definition (monoid). A monoid consists of:
- a set
M
- a binary operation
· : M × M → M
- an element
e ∈ M
such that
- the binary operation is associative, that is,
x · (y · z) = (x · y) · z, ∀ x, y, z ∈ M
- the element
e
is identity:x · e = x = e · x, ∀ x ∈ M
.
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{-# LANGUAGE DeriveFunctor #-} | |
{-# LANGUAGE InstanceSigs #-} | |
{-# LANGUAGE LambdaCase #-} | |
-- This script is based on Chris Taylor's gist: https://gist.github.com/chris-taylor/4745921 | |
-- | |
-- The main differences are: | |
-- 1. An interpretation in a different monad, the RWS monad. This idea was also was taken from somewhere else: | |
-- http://www.cs.uu.nl/docs/vakken/afp/assignment3.html | |
-- 2. An implementation based on free monads, see the `IOActionFreeMonad.hs` file. |
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import numpy as np | |
import pdb | |
from matplotlib import pyplot as plt | |
from scipy.stats import multivariate_normal | |
def gauss2d(mu, sigma, to_plot=False): | |
w, h = 100, 100 | |
std = [np.sqrt(sigma[0, 0]), np.sqrt(sigma[1, 1])] |
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"""This code is inspired by the homework 2 from CSC421/2516 Winter 2019, | |
but I'm taking a more functional approach. | |
http://www.cs.toronto.edu/~rgrosse/courses/csc421_2019/homeworks/hw2.pdf | |
http://www.cs.toronto.edu/~rgrosse/courses/csc421_2019/homeworks/maml.py | |
""" | |
import autograd.numpy as np | |
import autograd as ag |