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Top LangChain Books to Read in 2024

Marktechpost

The book covers the inner workings of LLMs and provides sample codes for working with models like GPT-4, BERT, T5, LLaMA, etc. It explains the fundamentals of LLMs and generative AI and also covers prompt engineering to improve performance. The book covers topics like Auto-SQL, NER, RAG, Autonomous AI agents, and others.

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Beyond ChatGPT; AI Agent: A New World of Workers

Unite.AI

Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication. Current Landscape of AI Agents AI agents, including Auto-GPT, AgentGPT, and BabyAGI, are heralding a new era in the expansive AI universe. Their primary focus is to minimize the need for human intervention in AI task completion.

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ChatGPT & Advanced Prompt Engineering: Driving the AI Evolution

Unite.AI

GPT-4: Prompt Engineering ChatGPT has transformed the chatbot landscape, offering human-like responses to user inputs and expanding its applications across domains – from software development and testing to business communication, and even the creation of poetry. Imagine you're trying to translate English to French.

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Google’s Dr. Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. Others, toward language completion and further downstream tasks. Then comes prompt engineering. People just basically try different prompts.

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Google’s Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

It came to its own with the creation of the transformer architecture: Google’s BERT, OpenAI, GPT2 and then 3, LaMDA for conversation, Mina and Sparrow from Google DeepMind. Others, toward language completion and further downstream tasks. Then comes prompt engineering. People just basically try different prompts.

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Deploy large models at high performance using FasterTransformer on Amazon SageMaker

AWS Machine Learning Blog

Prompt engineering Prompt engineering refers to efforts to extract accurate, consistent, and fair outputs from large models, such text-to-image synthesizers or large language models. For more information, refer to EMNLP: Prompt engineering is the new feature engineering.

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Dialogue-guided visual language processing with Amazon SageMaker JumpStart

AWS Machine Learning Blog

Additionally, you benefit from advanced features like auto scaling of inference endpoints, enhanced security, and built-in model monitoring. The pre-training of IDEFICS-9b took 350 hours to complete on 128 Nvidia A100 GPUs, whereas fine-tuning of IDEFICS-9b-instruct took 70 hours on 128 Nvidia A100 GPUs, both on AWS p4.24xlarge instances.