Search Results - "OpenAI"

Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4/ by Rothman, Denis

Generative AI with LangChain : Build Large Language Model (LLM) Apps with Python, ChatGPT, and Other LLMs. by Auffarth, Ben

Table of Contents: “…-- Comparing LangChain with other frameworks -- Summary -- Questions -- Chapter 3: Getting Started with LangChain -- How to set up the dependencies for this book -- pip -- Poetry -- Conda -- Docker -- Exploring API model integrations -- Fake LLM -- OpenAI -- Hugging Face -- Google Cloud Platform -- Jina AI -- Replicate -- Others -- Azure -- Anthropic -- Exploring local models -- Hugging Face Transformers -- llama.cpp -- GPT4All -- Building an application for customer service -- Summary -- Questions -- Chapter 4: Building Capable Assistants -- Mitigating hallucinations through fact-checking -- Summarizing information -- Basic prompting -- Prompt templates -- Chain of density -- Map-Reduce pipelines -- Monitoring token usage -- Extracting information from documents -- Answering questions with tools -- Information retrieval with tools -- Building a visual interface -- Exploring reasoning strategies -- Summary -- Questions -- Chapter 5: Building a Chatbot like ChatGPT -- What is a chatbot? …”
Full-text access

Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. by Kapoor, Amita

Table of Contents: “…-- How to deal with the highly correlated input state space -- How to deal with the problem of moving targets -- Reinforcement success in recent years -- Simulation environments for RL -- An introduction to OpenAI Gym -- Random agent playing Breakout -- Wrappers in Gym -- Deep Q-networks -- DQN for CartPole -- DQN to play a game of Atari -- DQN variants -- Double DQN -- Dueling DQN -- Rainbow -- Deep deterministic policy gradient -- Summary -- References -- Chapter 12: Probabilistic TensorFlow -- TensorFlow Probability -- TensorFlow Probability distributions -- Using TFP distributions -- Coin Flip Example -- Normal distribution -- Bayesian networks -- Handling uncertainty in predictions using TensorFlow Probability -- Aleatory uncertainty -- Epistemic uncertainty -- Creating a synthetic dataset -- Building a regression model using TensorFlow -- Probabilistic neural networks for aleatory uncertainty -- Accounting for the epistemic uncertainty -- Summary -- References.…”
Full-text access