I'm a machine learning engineer, quantitative analyst, and quantum computing enthusiast with a background in SaaS and venture capital.
In this guide we introduce the core concepts of natural language processing, including an overview of the NLP pipeline and useful Python libraries.
Inspired by Charles Darwin's theory of natural selection, genetic algorithms are a search heuristic that belong within the larger class of AI called evolutionary algorithms.
In this article we will look at several implementations of deep reinforcement learning with PyTorch.
In this article, we build a machine learning model to predict the likelihood that app users will enroll in a paid subscription.
In this article we introduce key concepts of the Python-based framework called Django for deploying machine learning models.
In this guide we introduce quantum programming with Qiksit, which is an open-source framework for working with quantum computers.
DeepDream is a powerful computer vision algorithm that uses a convolutional neural network to find and enhance certain patterns in images.
Transfer learning is a machine learning technique in which a pre-trained network is repurposed as a starting point for another similar task.
In this guide we look at how we can maximize revenue for an eCommerce business using a reinforcement learning algorithm called Thompson sampling.
In this article we look at how reinforcement learning can be used to optimize the business processes of an eCommerce warehouse.
In this article we introduce another important concept in the field of mathematics for machine learning: probability theory.
Continuing in our Mathematics for Machine Learning series, in this article we introduce an importance concept in machine learning: multivariate calculus.
In this article we introduce the first step in the mathematical foundation of machine learning: linear algebra.
In this article we look at how to program the D-Wave quantum annealer to solve several real-world problems.
In this article we're going to take our first steps in programming a quantum computer with Google's Cirq framework.
In this article we review a deep reinforcement learning algorithm called the Twin Delayed DDPG model, which can be applied to continuous action spaces.
In this guide we discuss several approaches to using quantum computing hardware to enhance machine learning algorithms.
In this guide we discuss several paradigms for quantum computing: gate-model quantum computing, adiabatic quantum computing, and quantum annealing.
Quantum systems are similar to classical probability distributions, but they have certain properties that make them unique.
A Tensor Processing Unit (TPU) is a custom computer chip designed by Google specifically for deep learning.
Edge AI means that AI algorithms are processed locally on a hardware device. The algorithms are using data that are created on the device.
Open sourced in November 2015 by Google, TensorFlow is currently the most popular framework for creating deep learning models.
