Shulou(Shulou.com)11/24 Report--

Wu Enda's new class is online, limited time and free! LangChain, ChatGPT API and diffusion model are all covered.

Wu Enda sent gospel to the AI community again.

Wu Enda announced the launch of three new generative AI courses on Twitter.

These three courses include--

1. Building a system using OpenAI's ChatGPT API: this course allows you to go beyond a single prompt and learn to build complex applications that use multiple API calls to LLM. At the same time, you will learn how to evaluate LLM output to ensure security and accuracy, and drive iterative improvements.

2. LangChain for LLM application development: by learning this powerful open source tool, you can build applications that use LLM, including the memory of chatbots, answers to questions on documents, and LLM agents that can decide what to do next.

3. How Diffusion models work: this course allows you to learn the technical details of Diffusion models, which support Midjourney, DALL E 2 and Stable Diffusion. You can also generate your own video game wizard Jupyter working code.

Note that these courses are time-limited and free, and each course lasts 1-1.5 hours.

In the course of building systems with ChatGPT API, you can learn how to automate complex workflows by continuously invoking large language models.

The contents include:

Build a chain of prompts that interact with previous prompts.

Build Python code to interact with existing and new prompts.

Build a customer service chat robot that uses the technology in the course.

These skills can be applied to practical scenarios, including classifying user queries as chat agent responses, evaluating the security of user queries, and handling tasks for thought chain and multi-step reasoning.

Among them, the hands-on examples make the concept easy to understand, while the built-in Jupyter Notebook allows you to seamlessly experiment with the code and labs introduced in the course.

This course is suitable for beginners. You can have a basic understanding of Python. It is also suitable for middle and senior machine learning engineers who want to learn LLM cutting-edge rapid engineering skills.

LangChain for LLM application development in this course, you can learn the basic skills of using the LangChain framework to extend the use cases and functions of language models in application development.

The details include:

Models, prompts, and parsers: call LLM, provide prompts, and parse responses

LLM memory: memory for storing conversations and managing limited context space

Chains: create a sequence of actions

Documentation Q & A: apply LLM to your proprietary data and use case requirements

Agent: exploring the powerful Development of LLM as a reasoning Agent

At the end of the course, you can have a model as a starting point for your own exploration of applied diffusion models.

This course will greatly help you expand the possibility of using powerful language models, and you can create incredible applications in a matter of hours.

This course is suitable for beginners. You can master the basic knowledge of Python.

How the Diffusion Model works

In this department, you can gain an in-depth understanding of the diffusion process and the model of implementing the diffusion process.

This course will not only simply introduce pre-built models or use API, but will also teach you to build diffusion models from scratch.

The details include:

Explore the frontier world of AI generation based on diffusion and create your own diffusion model from scratch.

In-depth understanding of the diffusion process and driving process model, beyond the pre-built model and API.

Practical coding skills are obtained by sampling in the laboratory, training the diffusion model, building a neural network for noise prediction and adding context for personalized image generation.

At the end of the course, you will have a model as a starting point for your own exploration of applied diffusion models.

Among them, the hands-on examples make the concept easy to understand, while the built-in Jupyter Notebook allows you to seamlessly experiment with the code and labs introduced in the course.

This is an intermediate course that requires knowledge of Python,Tensorflow or Pytorch.

Reference:

Https://www.deeplearning.ai/short-courses/

This article comes from the official account of Wechat: Xin Zhiyuan (ID:AI_era)

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