In this post, you will learn about supervised machine learning, and find out how genetic algorithms can be used to improve the performance of machine learning models by selecting the best subset of features from the provided input data. Along the way, you will get acquainted with the real-life ‘Zoo’ dataset and discover how to utilize Python code to create a…
Author: Eyal Wirsansky
Eyal Wirsansky is a senior software developer, an artificial intelligence consultant and a genetic algorithms specialist, helping developers and startup companies learn and utilize artificial intelligence techniques.
Eyal is the author of the book 'Hands-On Genetic Algorithms with Python' (Packt).
First DL4J Example – MNIST Classifier with a Single Layer MLP
Prerequisites: Introduction to DeepLearning4J, knowledge of Java. DLJ4 comes with a large amount of examples. Based on one of them, our first neural network code example is an MLP classifier for handwritten digit recognition. MNIST Classification Task The neural network in this example takes on the classification task of the MNIST database of handwritten digits. This database consists of numerous…
Introduction to DeepLearning4J
Prerequisites: Neural Networks, knowledge of Java. DeepLearning4J (or DL4J for short) is a Java based, commercial-grade, open-source, distributed deep-learning library. It can be used to create and combine various types of neural networks, train them and deploy the resulting models. A quick look at DL4J’s Github repository reveals that it is accompanied by ND4J, a scientific computing library with an…
Neural Networks Fundamentals
Prerequisites: Introduction to Supervised Learning. Inspired by the structure of the human brain, Neural Networks are among the most commonly used models for supervised learning tasks. The building blocks of these networks are simple ‘cells’, or ‘neurons’, which can be interconnected in various ways. Perceptron A single neuron has multiple inputs and a single output, and can be represented by…
Introduction to Supervised Learning
Prerequisites: None. What is Machine Learning? When we say, ‘Machine Learning’, the ‘Machine’ part usually insinuates a computer program that receives inputs and produces outputs. We often refer to this program as the ‘model’. Our goal is to ‘train’ that model to produce the correct outputs for given inputs, without explicitly programming it for that particular set of inputs. During…