GPT-4 Passes Bridgewater's Investment Associate Test
Earlier this month, the Co-CIO of Bridgewater revealed that OpenAI's GPT-3.5 has successfully passed Bridgewater’s investment associate test.
GPT-4 for Finance: Turning Financial Ratios into Insights
In this guide, we'll discuss how to use GPT-4 to summarize financial ratios and provide insightful analysis of how the data changed over the chosen time period.
GPT-4 for Financial Statements: Building an AI Analyst
In this guide, we discuss how build an AI analyst that uses GPT-4 to analyze financial statements, including income statements, balance sheets, and cash flow of public companies.
Time Series with TensorFlow: Prediction Intervals for Forecasting
In this article, we discuss the concept of prediction intervals, also known as uncertainty estimates, which give a range of prediction values with upper and lower bounds.
Time Series with TensorFlow: Building an Ensemble Model for Forecasting
In this article, we discuss how to use ensemble learning for the task of time series forecasting and combine their predictions to improve performance.
Time Series with TensorFlow: Replicating the N-BEATS Algorithm
In this article, we'll expand on our previous time series forecasting models and replicate the N-BEATS algorithm, which is a state-of-the-art forecasting algorithm.
Time Series with TensorFlow: Building a multivariate time series forecasting model
In this Time Series with TensorFlow article, we create a multivariate dataset, prepare it for modeling, and then create a simple dense model for forecasting.
Machine Learning for Finance: Price Prediction with Linear Regression
In this project we'll look at linear regression for price prediction, specifically the relationship between historical data and future price prediction.
Time Series with TensorFlow: Building an LSTM (RNN) for Forecasting
In this Time Series with TensorFlow article, we build a recurrent neural network (LSTM) model for forecasting Bitcoin price data.
Time Series with TensorFlow: Building a Convolutional Neural Network (CNN) for Forecasting
In this Time Series with TensorFlow article, we build a Conv1D (CNN) model for forecasting Bitcoin price data.
Time Series with TensorFlow: Building dense models with larger windows & horizons
In this article, we build two dense models with larger window & horizon sizes.
Time Series with TensorFlow: Building a dense model for Bitcoin price forecasting
In this article, we're going to create our first deep learning model for time series forecasting with Bitcoin price data.
Time Series with TensorFlow: Formatting Data with Windows & Horizons
In this article, we format our time series data with windows and horizons in order to turn the task of forecasting into a supervised learning problem.
Time Series with TensorFlow: Common Evaluation Metrics
In this article, we discuss several common evaluation metrics to evaluate our time series forecasting models.
Time Series with TensorFlow: Building a Naive Forecasting Model
In this article, we discuss the various modeling experiments we'll be running and then build a naive forecasting model for our Bitcoin price data.
Time Series with TensorFlow: Downloading & Formatting Historical Bitcoin Data
In this article, we'll start a new time series with TensorFlow project by importing historical Bitcoin data, visualizing it, and preparing it for modeling.
Deep Reinforcement Learning for Trading: Deploying the Algorithm at Interactive Brokers
In this section we'll finish our initial deep reinforcement learning trading algorithm by deploying it at a simulated account at Interactive Brokers.
Deep Reinforcement Learning for Trading: Using Gradient Ascent to Maximize Sharpe Ratio
In this section, the objective is to use reinforcement learning to maximize the Sharpe ratio using gradient ascent.
Deep Reinforcement Learning for Trading: Building a CNN for Probability
In this section, we're going to add another deep learning model to our trading algorithm and build a convolutional neural network (CNN).
Deep Reinforcement Learning for Trading: Building an LSTM for Price Prediction
In this guide we build an LSTM for price prediction in our deep reinforcement learning trading algorithm.
Deep Reinforcement Learning for Trading: Imports, Inputs, & Helper Functions
In this section we'll start with the imports, model and trading logic inputs, and helper functions that we'll need for this deep reinforcement learning for trading project.
Deep Reinforcement Learning for Trading: Project Overview
In this project we're going to build a deep reinforcement learning trading agent and deploy it in a simulated trading account at Interactive Brokers.
Nvidia Beats Q2 Earnings by 62%: Using AI to Analyze the Stock
Nvidia reported Q2 earnings after the close today, beating earnings by a staggering 68 percent from last year. In this article, we look at several takeaways from the quarter and look at ML-based estimates.
The Rise of Alternative Data & Machine Learning in Finance
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.
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.
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.
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.
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.
Introduction to Natural Language Processing (NLP) with Python
In this guide we introduce the core concepts of natural language processing, including an overview of the NLP pipeline and useful Python libraries.