Category: Scikit-Fuzzy

Mastering Machine Learning Algorithms

Artificial Intelligence, Convnet, Deep Learning, Keras, Machine Learning, Machine Learning Algorithms Addenda, Neural networks, Python, Scikit-Fuzzy, Scikit-Learn, Tensorflow, TensorflowNo Comments

Today I’ve published my latest book “Mastering Machine Learning Algorithms” (in a few days it will be available on all channels). From the back cover: Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the […]

Archimedes: a simple exercise with Keras and Scikit-Fuzzy

Deep Learning, Machine Learning, Neural networks, Python, Scikit-Fuzzy, Scikit-LearnNo Comments

This is a simple exercise, not a real, complete implementation. However I think it’s a good starting point if you want to use Keras in order to learn time sequences and Scikit-Fuzzy, to extract probabilistic rules (which descrive the evolution) from them. First of all, you need both packages: Keras (http://keras.io/) Theano (http://deeplearning.net/software/theano/) Scikit-Fuzzy (https://github.com/scikit-fuzzy/scikit-fuzzy) Moreover you need Numpy (I do suggest to use Anaconda as Python distribution, because it contains more or less everything you need) I start creating a sample sequence (in this case, very simple and reasonably predictable): import numpy as np class SampleGenerator(object): def __init__(self, num_samples, num_tests): self.num_samples = num_samples self.num_tests = num_tests def generate_discrete_sequence(self): symbols = (0, 1, 2, 3, 4, 5) data = [] data.append(np.random.randint(0, high=len(symbols))) for i in range(1, self.num_samples + self.num_tests): # Apply “probabilistic-logic” rules if data[i-1] == symbols[0]: if self.uniform_threshold(): data.append(symbols[2]) continue else: data.append(symbols[3]) continue if data[i-1] == symbols[1]: if self.uniform_threshold(): […]