In the realm of technology, becoming proficient in using the most recent generation of generative artificial intelligence (AI) tools has quickly become essential. These tools, like ChatGPT or Bard, have proven to be fun and have the ability to really help us in a variety of areas of our lives. To aid you in embracing this new era of generative AI, here is a compilation of top-notch courses that can serve as invaluable resources for honing your skills and staying at the forefront of this transformative technology.
One may improve their knowledge and ability to use the power of generative AI to its utmost potential by exploring these instructional materials, ensuring they make the most of these ground-breaking capabilities.
ChatGPT Prompt Engineering for Developers
One will discover how to use large language models (LLMs) to quickly build effective apps in the course “ChatGPT Prompt Engineering for Developers” taught by Andrew Ng and Isa Fulford. Users can now create capabilities that were either too expensive, too complicated or perhaps impossible by using the OpenAI API.
This course includes best practices for prompt engineering, insights into how LLMs work and examples of how to use LLM APIs for various tasks. Summarizing user evaluations, determining sentiments, identifying subjects, translating or fixing grammar in text, and expanding material by automatically producing emails are some of these tasks.
The course focuses on two essential principles for writing strong prompts and walks you through methodical prompt engineering. You will also have the chance to create a unique chatbot. You will learn useful skills in timely engineering with the help of various examples and a Jupyter Notebook environment for practical experience.
This training, which is being provided in collaboration with OpenAI, intends to give developers the knowledge and abilities they need to use LLMs effectively. This course is appropriate for you regardless of your level of Python proficiency or your interest in exploring cutting-edge prompt engineering and LLM usage.
LangChain for LLM Application Development
Enrol in the course “LangChain for LLM Application Development” to learn vital abilities for enhancing language models’ functionality in application development utilizing the LangChain framework. In this course, users will learn how to summon LLMs, write prompts, parse responses, use memory for conversations, create operation sequences, implement question-answering over documents, and explore the evolution of LLMs as reasoning agents.
Participants will have a model by the end of the course that may be used as a jumping-off point for more research and diffusion model application development. This hour-long workshop, taught by Andrew Ng and LangChain co-founder Harrison Chase, equips participants to build reliable applications quickly. The course is suitable for beginners; however, some familiarity with Python is helpful.
How Diffusion Models Work
Participants who want to create diffusion models from scratch should take the “How Diffusion Models Work” course. This intermediate-level course offers a thorough understanding of the models used in the diffusion process. Participants will learn to build their own diffusion model and acquire useful coding skills.
During the course, participants will:
- Develop their own diffusion model while exploring the field of diffusion-based generative AI.
- Beyond pre-built solutions and APIs, gain a thorough understanding of the diffusion process and the underlying models.
- Through labs on sampling, training diffusion models, creating neural networks for noise prediction, and incorporating context for personalized image generation, one can gain practical coding skills.
- Finish the course with a model that can serve as a starting point for further exploration of diffusion models in their own applications.
The session, led by Sharon Zhou, lasts one hour and focuses on creating, refining and optimizing diffusion models to enhance participants’ generative AI capabilities. Participants may easily comprehend and expand upon the concepts provided thanks to the use of practical examples and built-in Jupyter Notebooks.
Building Systems with the ChatGPT API
The “Building Systems with the ChatGPT API” course will teach participants how to automate intricate workflows by making a series of calls to a powerful language model. This succinct course improves development skills and increases productivity. Individuals will:
- Create a series of prompts that respond to earlier completions.
- Make technologies that allow Python programs to communicate with new prompts and completions.
- Apply the principles taught in the course to create a chatbot for customer support.
- Use these abilities in real-world situations, including user query classification, safety assessment and multi-step reasoning.
This one-hour session, taught by Ng of DeepLearning.AI and Fulford of OpenAI, expands on “ChatGPT Prompt Engineering for Developers” (not a prerequisite). Jupyter Notebooks and practical examples make it easier to understand and explore the course material.
Collaboration within the OpenAI community guarantees current best practices for optimum performance and responsible usage. The course is appropriate for those with a basic familiarity with Python as well as for intermediate and advanced ML engineers looking for cutting-edge, quick engineering skills for language models.
Introduction to ChatGPT
Join the "Introduction to ChatGPT" course by DataCamp to gain the knowledge needed for effective and responsible use of ChatGPT. This course covers ChatGPT's capabilities and restrictions and is appropriate for users of all skill levels. One can discover new integration opportunities, business use cases, and ChatGPT suggestions for best practices.
The course is divided into two modules: “Interacting with ChatGPT,” which is available for free, and “Adopting ChatGPT,” which is available for purchase or through a DataCamp subscription. By the end of the course, participants may feel confident applying ChatGPT in various situations, enhancing their speed and efficiency across a wide range of tasks.