Introduction to Portfolio Construction and Analysis: Risk & Returns
In this article, we'll introduce key concepts of risk and return in portfolio analysis, including Value-at-Risk, Conditional Value-at-Risk, and more.
I'm a machine learning engineer, quantitative analyst, and quantum computing enthusiast with a background in SaaS and venture capital.
In this article, we'll introduce key concepts of risk and return in portfolio analysis, including Value-at-Risk, Conditional Value-at-Risk, and more.
In this article, we discuss two important topics in reinforcement learning: Q-learning and deep Q-learning.
In this guide, we discuss two types of GANs that allow you to control the output of the model: conditional GANs (cGANs) and controllable generation.
Data visualization is an essential step in quantitative analysis. In this guide we introduce the most popular data visualization libraries in Python.
In this article, we discuss the Wasserstein loss function for Generative Adversarial Networks (GANs), which solves a common issue that arises during the training process.
In this article, we discuss the key components of building a DCGAN for the purpose of image generation. This includes activation functions, batch normalization, convolutions, pooling and upsampling, and transposed convolutions.
Generative Adversarial Networks, or GANs, are an emergent class of deep learning that have been used for everything from creating deep fakes, synthetic data, creating NFT art, and more.
In this article we provide an overview of deep reinforcement learning for trading. Reinforcement learning is the computational science of decision making.
In this article on SQL for data science, we discuss how to merge and combine data from multiple sources using subqueries and joins.
In this article, we discuss how to filter, sort, aggregate, calculate, and group data with SQL.
In this article, we introduce SQL for data science, including how to select and retrieve data, common SQL syntax, and more.
Dynamic programming is fundamental to many reinforcement learning algorithms. In this article, we discuss how it can be used for policy evaluation and control.
In this article, we discuss fundamental concepts in reinforcement learning including policies, value functions, and Bellman equations.
In this article, we discuss several fundamental concepts of reinforcement learning including Markov decision processes, the goal of reinforcement learning, and continuing vs. episodic tasks.
In this article, we introduce fundamental concepts of reinforcement learning—including the k-armed bandit problem, estimating the action-value function, and the exploration vs. exploitation dilemma.
In this article, we'll introduce an important concept in quantitative modeling: regression models, which are an important tool for predictive analytics.
In this article, we introduce a subset of quantitative modeling: probabilistic models, which have a key component of incorporating uncertainty into them.
In this article, we introduce key concepts of quantitative modeling for finance. This includes the modeling workflow, common vocabulary, and several mathematical functions.
When building production-level machine learning systems, it's important to remember that the model is only a small part of a much larger ecosystem.
In this article, we discuss one of the most widely used applications of machine learning in our everyday lives: recommendation systems.
In this guide, we'll discuss the key concepts and use cases of data lakes vs. data warehouses with Google Cloud Platform.
In this introduction to data engineering, we discuss key concepts including raw data sources, data lakes, and data warehouses.
In this article, we'll review the theory and intuition of the Capital Asset Pricing Model (CAPM) and then discuss how to calculate it with Python.
In this article, we review how to use sequence models such as recurrent neural networks (RNNs) and LSTMs for time series forecasting with TensorFlow.
In this article, we'll introduce building time series models with TensorFlow, including best practices for preparing time series data.
In this article, we introduce how to use TensorFlow and Keras for natural language processing (NLP).
In this article, we'll review how to use TensorFlow for computer vision using convolutional neural networks (CNNs).
In order to overcome the limitations of data scarcity, privacy, and costs, GANs for generating synthetic financial data may be essential in the adoption of AI.
In this article, we introduce key concepts of TensorFlow Quantum (TFQ), which is a framework for building near-term quantum machine learning applications.
In this article, we introduce the Quantopian trading platform for developing and backtesting trading algorithms with Python.
In this article, we review key mathematical techniques to analyze and solve problems with quantum computing.
In this guide we'll review key concepts regarding the application of deep learning for natural language processing.
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.