All courses
Jay AlammarDeepLearning.AI
In this course, you will learn how to implement basic keyword search, enhance it with the rerank method, and use embeddings for dense retrieval. You will gain hands-on practice with large amounts of data and learn to overcome challenges like varying search results and accuracy. By the end of the course, you will be able to implement language model-powered search into your website or project.
Josh StarmerDeepLearning.AI
In this course, you will delve into the attention mechanism, a key component of transformers, and learn how to implement it using PyTorch. You'll explore the relationships between word embeddings, positional embeddings, and attention, and understand the roles of Query, Key, and Value matrices. The course covers self-attention, masked self-attention, and cross-attention, providing a comprehensive understanding of how these concepts are incorporated into transformers. By the end, you'll be equipped with the knowledge to build reliable and scalable AI applications.
Milan CucukMaven
In this immersive course, you'll learn how to write a story, turn it into a script, create a storyboard, and produce a visually stunning AI short film or trailer. You'll use tools like Midjourney for storyboarding, Runway for generating footage, and Eleven Labs for creating lifelike voices. By the end of the course, you'll have a completed script, an AI-enhanced storyboard, an AI short film or trailer, and an electronic press kit to attract attention and talent to your AI Cinema.
Sharon ZhouDeepLearning.AI
In this specialization, you will learn to understand GAN components, build basic and advanced GANs using PyTorch, and control your GAN to build conditional GANs. You will compare generative models, use the FID method to assess GAN fidelity and diversity, and learn to detect bias in GANs. Additionally, you will explore GAN applications in data augmentation and privacy preservation, and implement Pix2Pix and CycleGAN for image translation.
Antje BarthDeepLearning.AI
This course provides a comprehensive understanding of generative AI, covering key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection to performance evaluation and deployment. You will learn about the transformer architecture, empirical scaling laws, and state-of-the-art training, tuning, inference, tools, and deployment methods. The course also discusses the challenges and opportunities that generative AI creates for businesses, with insights from industry researchers and practitioners.
Laurence MoroneyDeepLearning.AI
In this course, you will learn to integrate generative AI tools into your software development process, enhancing your coding efficiency and creativity. Through hands-on projects, you will gain techniques for using AI to assist with common development tasks, from writing and testing code to creating documentation and managing dependencies. The course is designed for both individual developers and team members looking to integrate AI tools into their workflow, helping you become a faster, more productive worker.
Sharon ZhouDeepLearning.AI
In this specialization, you will learn to understand GAN components, build basic and advanced GANs using PyTorch, and control your GAN to build conditional GANs. You will compare generative models, use the FID method to assess GAN fidelity and diversity, and learn to detect bias in GANs. Additionally, you will explore GAN applications in data augmentation and privacy preservation, and implement Pix2Pix and CycleGAN for image translation.
edX LearningedX
Welcome to a transformative learning experience designed to enhance your personal and professional growth. Our course empowers you to foster a growth mindset, embrace continuous learning, and master learning how to learn. Gain practical knowledge that you can apply immediately in your personal and professional life, with AI assistance to guide your learning journey.
