I'm a machine learning engineer, quantitative analyst, and quantum computing enthusiast with a background in SaaS and venture capital.
In this guide, we'll discuss what alternative data is, examples and challenges of alt-data, and how machine learning can be used to extract insights and signals from the noise.
In this article we apply an unsupervised learning technique, K-means clustering, to a group of companies imported from Yahoo Finance.
In this guide, we're going to review an interesting application of AI for trading and investing: machine learning for multiday stock estimates.
In this guide to blockchain analytics, we discuss 14 key terms that every crypto on-chain analyst, trader, and investor should know.
In this article, we review time series analysis with Python, including Pandas for time series data and time series analysis techniques
In this guide, we introduce the fundamentals of Python programming for finance, including two key Python libraries: NumPy and Pandas.
In this guide, we'll discuss exactly what on-chain analysis is and how you can it to improve your crypto trading and investing.
In this guide, we discuss how traders and investors can use AI and machine learning to rank stocks, otherwise known as predictive equity ranking.
In this guide, we discuss variational autoencoders, which combine techniques from deep learning and Bayesian machine learning, specifically variational inference.
In this guide, we discuss how traders and investors can use sentiment analysis and natural language processing (NLP) for SEC filings to speed up their research process.
In this guide, we discuss 8 applications of AI and machine learning for trading and investing. This includes sentiment analysis, return estimates, and more.
In this article, we discuss various applications of classification-based machine learning in finance, including logistic regression for predicting asset returns.
A recurrent neural network (RNN) attempts to model time-based or sequence-based data. An LSTM network is a type of RNN that uses special units as well as standard units.
In this article on natural language processing, we discuss how to use the Naive Bayes formula for the purpose of sentiment analysis.
In this guide, we discuss the application of deep reinforcement learning to the field of algorithmic trading.
In this article, we discuss how to use natural language processing and logistic regression for the purpose of sentiment analysis.
In this article we look at how to build a reinforcement learning trading agent with deep Q-learning using TensorFlow 2.0.
In this article, we discuss two key concepts in portfolio optimization: Markovitz optimization and the Efficient Frontier.
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.
