What is Crypto On-Chain Analysis?
In this guide, we'll discuss exactly what on-chain analysis is and how you can it to improve your crypto trading and investing.
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Peter Foy
Articles
By
Peter Foy
China Releases Largest Language Model to Date - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about natural language processing, sentiment analysis for SEC filings, and the most in-demand AI jobs.
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MLQ
How to Use AI and Machine Learning to Rank Stocks
In this guide, we discuss how traders and investors can use AI and machine learning to rank stocks, otherwise known as predictive equity ranking.
By
Peter Foy
Generative Models
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Generative Modeling: What is a Variational Autoencoder (VAE)?
In this guide, we discuss variational autoencoders, which combine techniques from deep learning and Bayesian machine learning, specifically variational inference.
By
Peter Foy
Sentiment Analysis and Natural Language Processing (NLP) for SEC Filings
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.
By
Peter Foy
OpenAI Fund, AI for Trading, and 3D Mapping the Universe - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about OpenAI, AI for trading and investing, and 3D mapping the universe.
By
MLQ
8 Applications of AI & Machine Learning for Trading and Investing
In this guide, we discuss 8 applications of AI and machine learning for trading and investing. This includes sentiment analysis, return estimates, and more.
By
Peter Foy
Conversation Technology, Chip Performance, and the Vertex Platform - This Week in AI
Welcome to our This Week in AI roundup. This week we have several announcements from Google's annual developer conference, Google I/O.
By
MLQ
Classification-Based Machine Learning for Finance
In this article, we discuss various applications of classification-based machine learning in finance, including logistic regression for predicting asset returns.
By
Peter Foy
An Introduction to Recurrent Neural Networks & LSTMs
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.
By
Peter Foy
AI Innovations & Quantum Advances - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about AI innovations, quantum advances, data roadblocks in AI, and more.
By
MLQ
Naive Bayes for Sentiment Analysis & Natural Language Processing (NLP)
In this article on natural language processing, we discuss how to use the Naive Bayes formula for the purpose of sentiment analysis.
By
Peter Foy
Deep Reinforcement Learning for Trading: Strategy Development & AutoML
In this guide, we discuss the application of deep reinforcement learning to the field of algorithmic trading.
By
Peter Foy
How AI is Driving Revenue Growth - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about new open-source tools, AI for credit risk, how AI is driving revenue growth, and more.
By
MLQ
Natural Language Processing (NLP) for Sentiment Analysis with Logistic Regression
In this article, we discuss how to use natural language processing and logistic regression for the purpose of sentiment analysis.
By
Peter Foy
Deep Reinforcement Learning for Trading with TensorFlow 2.0
In this article we look at how to build a reinforcement learning trading agent with deep Q-learning using TensorFlow 2.0.
By
Peter Foy
Forbes AI 50, Hyperautomation, and ML-Based Lending - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about the Forbes AI 50 list, hyperautomation, a new ML framework from Facebook, and Shopify's ML-based lending.
By
MLQ
Quantitative Finance
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Introduction to Markowitz Portfolio Optimization and the Efficient Frontier
In this article, we discuss two key concepts in portfolio optimization: Markovitz optimization and the Efficient Frontier.
By
Peter Foy
This Week in AI
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EU Proposes AI Rules, Unity Releases Synthetic Data, and Qiskit Machine Learning - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about new proposed AI rules by the European Commission, synthetic datasets, and quantum machine learning.
By
MLQ
Quantitative Finance
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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.
By
Peter Foy
Deep Reinforcement Learning: Guide to Deep Q-Learning
In this article, we discuss two important topics in reinforcement learning: Q-learning and deep Q-learning.
By
Peter Foy
This Week in AI
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AI Legislation in the EU, Drug Discovery, and Robotic Pizza Deliveries - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about new AI rules in the EU, AI for drug discovery, and robotic pizza deliveries.
By
MLQ
Generative Models
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Introduction to Conditional GANs (cGANs) & Controllable Generation
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.
By
Peter Foy
This Week in AI
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Enterprise AI Trends, Top 100 AI Companies, and Deep Learning Papers - This Week in AI
Welcome to our This Week in AI roundup. This week we have stories about enterprise AI trends, the top 100 AI companies, deep learning papers, and more.
By
MLQ
Python for Finance
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Python for Finance: Data Visualization
Data visualization is an essential step in quantitative analysis. In this guide we introduce the most popular data visualization libraries in Python.
By
Peter Foy
Generative Models
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Introduction to Wasserstein GANs with Gradient Penalty
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.
By
Peter Foy
This Week in AI
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Universal Basic AI Income, GPT-3, and Nanoparticles - This Week in AI
Welcome to our first edition This Week in AI roundup. This week we have stories about universal basic AI income, GPT-3, synthetic data, and nanoparticles.
By
MLQ
Generative Models
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Introduction to Deep Convolutional GANs (DCGANs)
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.
By
Peter Foy
Generative Models
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Introduction to Generative Adversarial Networks (GANs): Intuition & Theory
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.
By
Peter Foy
An Overview of Deep Reinforcement Learning for Trading
In this article we provide an overview of deep reinforcement learning for trading. Reinforcement learning is the computational science of decision making.
By
Peter Foy
SQL
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SQL for Data Science: Subqueries and Joins
In this article on SQL for data science, we discuss how to merge and combine data from multiple sources using subqueries and joins.
By
Peter Foy
SQL
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SQL for Data Science: Filtering, Sorting, and Calculating Data
In this article, we discuss how to filter, sort, aggregate, calculate, and group data with SQL.
By
Peter Foy
SQL
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SQL for Data Science: Selecting and Retrieving Data
In this article, we introduce SQL for data science, including how to select and retrieve data, common SQL syntax, and more.
By
Peter Foy
Fundamentals of Reinforcement Learning: Dynamic Programming
Dynamic programming is fundamental to many reinforcement learning algorithms. In this article, we discuss how it can be used for policy evaluation and control.
By
Peter Foy
Fundamentals of Reinforcement Learning: Policies, Value Functions & the Bellman Equation
In this article, we discuss fundamental concepts in reinforcement learning including policies, value functions, and Bellman equations.
By
Peter Foy
Fundamentals of Reinforcement Learning: Markov Decision Processes
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.
By
Peter Foy
Fundamentals of Reinforcement Learning: Estimating the Action-Value Function
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.
By
Peter Foy
Quantitative Modeling
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Introduction to Quantitative Modeling: Regression Models
In this article, we'll introduce an important concept in quantitative modeling: regression models, which are an important tool for predictive analytics.
By
Peter Foy
Quantitative Modeling
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Introduction to Quantitive Modeling: Probabilistic Models
In this article, we introduce a subset of quantitative modeling: probabilistic models, which have a key component of incorporating uncertainty into them.
By
Peter Foy
Quantitative Modeling
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Introduction to Quantitative Modeling: Linear Models
In this article, we introduce key concepts of quantitative modeling for finance. This includes the modeling workflow, common vocabulary, and several mathematical functions.
By
Peter Foy
Machine Learning
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How to Build Production-Level Machine Learning Systems
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.
By
Peter Foy
TensorFlow
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Introduction to Recommendation Systems with TensorFlow
In this article, we discuss one of the most widely used applications of machine learning in our everyday lives: recommendation systems.
By
Peter Foy
Data Engineering
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Data Lakes vs. Data Warehouses: Key Concepts & Use Cases with GCP
In this guide, we'll discuss the key concepts and use cases of data lakes vs. data warehouses with Google Cloud Platform.
By
Peter Foy
Data Engineering
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Introduction to Data Engineering, Data Lakes, and Data Warehouses
In this introduction to data engineering, we discuss key concepts including raw data sources, data lakes, and data warehouses.
By
Peter Foy
Python for Finance
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Introduction to the Capital Asset Pricing Model (CAPM) with Python
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.
By
Peter Foy
TensorFlow
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Recurrent Neural Networks (RNNs) and LSTMs for Time Series Forecasting
In this article, we review how to use sequence models such as recurrent neural networks (RNNs) and LSTMs for time series forecasting with TensorFlow.
By
Peter Foy
Machine Learning
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Introduction to Sequences and 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.
By
Peter Foy
Natural Language Processing
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Introduction to Natural Language Processing (NLP) with TensorFlow
In this article, we introduce how to use TensorFlow and Keras for natural language processing (NLP).
By
Peter Foy
Deep Learning
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Introduction to Convolutional Neural Networks (CNNs) with TensorFlow
In this article, we'll review how to use TensorFlow for computer vision using convolutional neural networks (CNNs).
By
Peter Foy
Machine Learning for Finance
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Synthetic Financial Data with Generative Adversarial Networks (GANs)
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.
By
Peter Foy