This is the concluding post in a series of four posts. In the previous post, Going Deeper with Tribuo, we created a Random Forest model that was trained to predict the quality of red wine based on the values of eleven given characteristics. This time, we will use this model to create a REST service that will respond with 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).
Going Deeper with Tribuo—Regression, Provenance and Model Serialization
This is the third post in a series of four posts. In the previous post, First Tribuo Example, we saw how to implement a wine quality classifier with Tribuo, a Java-based Machine Learning library. This time, we will use the same dataset to demonstrate how to implement a regressor, and along the way discover a few more capabilities of Tribuo.…
First Tribuo Example—Wine Quality Classifier
This is the second post in a series of four posts. As we saw in Introduction to Supervised Learning, one of the basic machine learning tasks is classification, where we need to map a set of inputs (usually referred to as ‘features’) into two or more categories (or ‘classes’). While typically data for such tasks needs to be collected and…
Introduction to Oracle Tribuo
This is the opening post in a series of four posts. Oracle Tribuo is a Java based, open-source, general purpose machine learning library. It provides tools for various machine learning tasks, such as classification, regression and clustering, as well as natural language processing (NLP). In addition, Tribuo enables Java programs to use models that were trained by Python libraries, such as…
Enhancing Machine Learning Models using Genetic Algorithms with Python
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…
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…