## Understanding Transformers & the Architecture of LLMs

In this guide, we discuss the foundations of LLMs and the Transformer architecture.

In this guide, we discuss the foundations of LLMs and the Transformer architecture.

In this article, we fine-tune GPT-3 on an earnings call transcript to write a summary and answer questions about the call.

In this guide, we discuss how to use embeddings to create a factual GPT-3 question-and-answer bot.

In this article, we'll discuss GPT-3: including its key concepts, how it works, use cases, fine-tuning, and more.

In this guide, we'll discuss everything you need to know about Large Language Models (LLMs), including key terms, algorithms, fine-tuning, and more.

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.

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.

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.

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.

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.

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.

In this Time Series with TensorFlow article, we build a recurrent neural network (LSTM) model for forecasting Bitcoin price data.

In this Time Series with TensorFlow article, we build a Conv1D (CNN) model for forecasting Bitcoin price data.

This guide is discuss the application of neural networks to reinforcement learning. Deep reinforcement learning is at the cutting edge of AI.

In this article, we build two dense models with larger window & horizon sizes.

In this article, we're going to create our first deep learning model for time series forecasting with Bitcoin price data.

Convolutional neural networks (CNNs) are a sub-class of the deep learning family that's commonly applied to image data.

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.

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.

In this section we'll finish our initial deep reinforcement learning trading algorithm by deploying it at a simulated account at Interactive Brokers.

In this section, the objective is to use reinforcement learning to maximize the Sharpe ratio using gradient ascent.

In this guide we build an LSTM for price prediction in our deep reinforcement learning trading algorithm.

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.

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.

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.

Generative Models
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In this guide, we discuss variational autoencoders, which combine techniques from deep learning and Bayesian machine learning, specifically variational inference.

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 guide, we discuss the application of deep reinforcement learning to the field of algorithmic trading.

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 important topics in reinforcement learning: Q-learning and deep Q-learning.

Generative Models
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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.

Generative Models
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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 Models
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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.

TensorFlow
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In this article, we review how to use sequence models such as recurrent neural networks (RNNs) and LSTMs for time series forecasting with TensorFlow.

Deep Learning
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In this article, we'll review how to use TensorFlow for computer vision using convolutional neural networks (CNNs).

In this article we will look at several implementations of deep reinforcement learning with PyTorch.

Deep Learning
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DeepDream is a powerful computer vision algorithm that uses a convolutional neural network to find and enhance certain patterns in images.

Machine Learning
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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 article we review a deep reinforcement learning algorithm called the Twin Delayed DDPG model, which can be applied to continuous action spaces.

Machine Learning
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A Tensor Processing Unit (TPU) is a custom computer chip designed by Google specifically for deep learning.