Fine-Tuning GPT-3: Building an Earnings Call Assistant
In this article, we fine-tune GPT-3 on an earnings call transcript to write a summary and answer questions about the call.
By
Peter Foy
Deep Learning
By
Peter Foy
Building a Custom GPT-3 Q&A Bot Using Embeddings
In this guide, we discuss how to use embeddings to create a factual GPT-3 question-and-answer bot.
By
Peter Foy
What is GPT-3? Key Concepts & Use Cases
In this article, we'll discuss GPT-3: including its key concepts, how it works, use cases, fine-tuning, and more.
By
Peter Foy
What is a Large Language Model (LLM)?
In this guide, we'll discuss everything you need to know about Large Language Models (LLMs), including key terms, algorithms, fine-tuning, and more.
By
Peter Foy
Introduction to Deep Learning with PyTorch
Developed as an open source project by the Facebook AI team, PyTorch was released in 2017 and has been making a big impact in the deep learning community.
By
Peter Foy
What are Generative Adversarial Networks (GANs)?
The idea of GANs is that we have two neural networks, a generator and a discriminator, which learn from each other to generate realistic samples from data.
By
Peter Foy
What is Generative AI? Key Concepts & Use Cases
In this article, we'll discuss key concepts about generative AI, including what it is, generative AI models, generative AI startups to watch, and more.
By
Peter Foy
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.
By
Peter Foy
What is ChatGPT? Key Concepts & Use Cases
In this guide, we'll review the chatbot everyone on the internet is talking about: ChatGPT. We'll discuss what ChatGPT is, its limitations, key concepts, use cases, and more.
By
Peter Foy
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.
By
Peter Foy
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.
By
Peter Foy
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.
By
Peter Foy
Guide to Deep Reinforcement Learning: Key Concepts & Use Cases
This guide is discuss the application of neural networks to reinforcement learning. Deep reinforcement learning is at the cutting edge of AI.
By
Peter Foy
Time Series with TensorFlow: Building dense models with larger windows & horizons
In this article, we build two dense models with larger window & horizon sizes.
By
Peter Foy
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.
By
Peter Foy
What are Convolutional Neural Networks? A Guide to CNNs
Convolutional neural networks (CNNs) are a sub-class of the deep learning family that's commonly applied to image data.
By
Peter Foy
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.
By
Peter Foy
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.
By
Peter Foy
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.
By
Peter Foy
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.
By
Peter Foy
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.
By
Peter Foy
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.
By
Peter Foy
How to Build a Convolutional Neural Network in Python with Keras
In this article, we provide a step-by-step tutorial for building your first CNN in Python with Keras, which high-level neural network API written in Python.
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
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