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@wojteklu
wojteklu / clean_code.md
Last active May 5, 2024 00:42
Summary of 'Clean code' by Robert C. Martin

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.


General rules

  1. Follow standard conventions.
  2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
  3. Boy scout rule. Leave the campground cleaner than you found it.
  4. Always find root cause. Always look for the root cause of a problem.

Design rules

@gabrieleangeletti
gabrieleangeletti / rbm_after_refactor.py
Last active July 27, 2021 14:32
Restricted Boltzmann Machine implementation in TensorFlow, before and after code refactoring. Blog post: http://blackecho.github.io/blog/programming/2016/02/21/refactoring-rbm-tensor-flow-implementation.html
import tensorflow as tf
import numpy as np
import os
import zconfig
import utils
class RBM(object):
@ottokart
ottokart / nn.py
Last active August 27, 2021 05:52
3-layer neural network example with dropout in 2nd layer
# Tiny example of 3-layer nerual network with dropout in 2nd hidden layer
# Output layer is linear with L2 cost (regression model)
# Hidden layer activation is tanh
import numpy as np
n_epochs = 100
n_samples = 100
n_in = 10
n_hidden = 5
@gabrieleangeletti
gabrieleangeletti / autoencoder.py
Last active October 15, 2019 15:16
Denoising Autoencoder implementation using TensorFlow.
import tensorflow as tf
import numpy as np
import os
import zconfig
import utils
class DenoisingAutoencoder(object):
""" Implementation of Denoising Autoencoders using TensorFlow.
@karpathy
karpathy / min-char-rnn.py
Last active May 4, 2024 17:44
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
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)