Data

Ofer MendelevitchDeepLearning.AI

This course offers a comprehensive exploration of embedding models, focusing on their architecture and implementation. Participants will learn about word and sentence embeddings, dual encoder models, and the use of contrastive loss to enhance question-answer retrieval applications. The course provides a hands-on approach to understanding the technical concepts behind embedding models, making it ideal for data scientists, machine learning engineers, and NLP enthusiasts.

Sung KimDeepLearning.AI

This course provides a comprehensive understanding of pretraining large language models. You'll learn to prepare data, configure models, and assess performance, with a focus on cost-effective strategies. The course covers creating high-quality datasets, using the Hugging Face library, and evaluating model performance with benchmark tasks.

Richmond AlakeDeepLearning.AI

In this course, you will learn how to use pre-filtering, post-filtering, and projection techniques to optimize query processing and output. You will also explore prompt compression techniques to handle long and expensive prompts in large-scale applications. Additionally, you will learn to combine vector search capabilities with traditional database operations to build efficient and cost-effective RAG applications. This course is ideal for Python developers, database enthusiasts, and AI practitioners looking to enhance their skills in these areas.

Miguel AmigotedX

In this course, you will gain a deep understanding of ChatGPT's capabilities and limitations, including its use cases in data, coding, and tech. You will develop proficiency in using ChatGPT for generating code programmatically, from ideation to testing and debugging. Explore the applications of ChatGPT in architecture and design, code rewriting, and other areas of programming. Learn how to integrate ChatGPT into your programming workflow and leverage its human-like conversation capabilities to enhance productivity.

Sebastian WitalecDeepLearning.AI

In this course, you will gain the knowledge to make informed decisions about when to apply vector databases to your applications. You will explore how to use vector databases and LLMs to gain deeper insights into your data, build labs that show how to form embeddings, and use several search techniques to find similar embeddings. Additionally, you will explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.

Abby MorganDeepLearning.AI

In this course, you will learn to prompt various vision models such as Meta’s Segment Anything Model (SAM), OWL-ViT, and Stable Diffusion 2.0. You will explore techniques like image generation, image segmentation, object detection, and in-painting. Additionally, you will learn to fine-tune diffusion models using DreamBooth and track your experiments with Comet. This hands-on course is designed to help you get started with prompting vision models and enhance your image generation capabilities.

Carey PhelpsDeepLearning.AI

In this course, you will learn to instrument a Jupyter notebook, manage hyperparameter configurations, log run metrics, collect artifacts for dataset and model versioning, and log experiment results. Additionally, you will learn to trace prompts and responses to LLMs over time in complex interactions. By the end of the course, you will have a systematic workflow to boost your productivity and accelerate your journey toward breakthrough results.

Miguel AmigotedX

In this course, you will gain a deep understanding of ChatGPT's capabilities and limitations, including its use cases in data, coding, and tech. You will develop proficiency in using ChatGPT for generating code programmatically, from ideation to testing and debugging. Explore the applications of ChatGPT in architecture and design, code rewriting, and other areas of programming. Learn how to integrate ChatGPT into your programming workflow and leverage its human-like conversation capabilities to enhance productivity.