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chris-chris / simple_gradient_descent.py
Last active Sep 11, 2017
simple 1-step gradient descent
View simple_gradient_descent.py
# From udacity Machine Learning Nanodegree course
import numpy as np
# Define sigmoid function
def sigmoid(x):
return 1/(1+np.exp(-x))
# Derivative of the sigmoid function
def sigmoid_derivative(x):
@vasanthk
vasanthk / System Design.md
Last active Sep 21, 2021
System Design Cheatsheet
View System Design.md

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

  1. Clarify and agree on the scope of the system
  • User cases (description of sequences of events that, taken together, lead to a system doing something useful)
    • Who is going to use it?
    • How are they going to use it?
@karpathy
karpathy / min-char-rnn.py
Last active Sep 21, 2021
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
View min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np
# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
@karpathy
karpathy / gist:587454dc0146a6ae21fc
Last active Sep 3, 2021
An efficient, batched LSTM.
View gist:587454dc0146a6ae21fc
"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code
class LSTM:
@staticmethod
def init(input_size, hidden_size, fancy_forget_bias_init = 3):