The Top 10 Learning Resources for ChatGPT and Generative AI

A ranking of the top 10 educational programmes for ChatGPT and generative AI that cover a wide range of subjects

A language model under the more general heading of Generative AI is ChatGPT, created by OpenAI. Generative AI is a category of artificial intelligence models and algorithms that may create text, images, music, or videos based on patterns in existing data. Deep learning techniques are the main foundation of both ChatGPT and Generative AI.Generative AI and ChatGPT have swept the globe. How, then, can this novel technology generate all the hullabaloo? To build chatbots and virtual assistants that converse with users in natural language, respond to their questions, provide information, and even make tailored recommendations, applications of ChatGPT and Generative AI provide the fundamental understanding. Since these two technological developments are revolutionising numerous industries, numerous platforms have developed courses that give a thorough introduction and give programmers the resources they need to create sophisticated conversational AI. The top 10 ChatGPT and generative AI courses are listed below:

1. The official course "Building AI Chatbots with ChatGPT" by OpenAI focuses on utilising ChatGPT to build AI chatbots. The course walks students through the creation of chatbots and gives them a thorough understanding of ChatGPT. Data collecting, fine-tuning, and ethical issues are among the subjects covered. Learners acquire the skills necessary to build entertaining and contextually appropriate chatbots through real-world examples and hands-on exercises.

2. Andrew Ng's "Deep Learning Specialisation" on Coursera is a well-liked and thorough course that covers deep learning techniques. Andrew Ng, a well-known AI expert, teaches fundamental subjects like neural networks, deep learning architectures, and deep learning optimisation in this specialisation.Advanced methods including convolutional networks, recurrent networks, and sequence models are also covered in the course. The course provides real experience and educates students with the abilities to develop and deploy deep learning models through hands-on programming assignments and examinations.

3. The University of Washington's "Advanced Topics in Conversational AI" course on edX focuses on sophisticated ideas and methods in conversational AI. The course, taught by qualified professors, includes subjects including dialogue control, language generation, and interpreting natural language. Students study cutting-edge methods and scientific developments in the area. Participants learn how to create sophisticated conversational AI systems through real assignments and projects.

4.The University of Michigan's "Natural Language Processing Specialisation" on Coursera is a thorough course that focuses on NLP methods. It covers a wide range of topics, including sentiment analysis, part-of-speech tagging, and machine translation, and is led by industry specialists. Assignments and projects that include creating real-world NLP applications give students practical experience.

5.DeepLearning's "Applied AI with DeepLearning.AI" is number five.Applied AI approaches are the topic of a variety of online courses offered by the AI programme. The course covers subjects including reinforcement learning, natural language processing, and computer vision. The courses are taught by professionals in the field and offer realistic coding exercises and real-world assignments to develop useful AI applications.

6 David Foster's online course "Generative Deep Learning" addresses several generative models, such as GANs, VAEs, and autoregressive models. The course covers subjects like music composition, text generation, and visual synthesis. Learners have practical experience using and training generative models through projects and exercises in practical coding.

7. Stanford University's "Generative Models" lecture series, which is accessible on YouTube, offers a thorough introduction to generative models. The course, taught by Stanford faculty, includes both conventional and deep learning-based methods of generative modelling. The subjects covered include deep autoregressive models, generative adversarial networks (GANs), variational autoencoders, and graphical models. The lectures provide insights into image generation, text generation, and other topics by delving into theoretical ideas and real-world applications.

8 The University of Amsterdam's "Deep Generative Models" course, which is accessible on YouTube, focuses on examining several deep generative models. The course, taught by knowledgeable educators, includes subjects including flow-based models, generative adversarial networks, and variational autoencoders (VAEs). It offers understanding into the theory and real-world applications of these models for jobs like picture creation and unsupervised learning.

9. New York University's "GANs Specialisation" on Coursera provides a thorough course on Generative Adversarial Networks (GANs). The specialisation, which is run by renowned professors, includes subjects like GAN architecture, training methods, and applications in many fields like computer vision and natural language processing. Learners receive real experience in establishing and fine-tuning GAN models through practical assignments and projects.

10. Fast.ai's "Practical Deep Learning for Coders" emphasises hands-on coding and real-world applications, and it is a very practical course. The course, taught by specialists in the field, covers fundamental ideas in deep learning, such as convolutional and recurrent neural networks. It offers a useful top-down methodology that enables students to develop and use deep learning models fast.