Description
Multimodal AI Essentials: Merging Text, Image, and Audio for Next-Generation AI Applications. This course provides the knowledge and skills necessary to implement multimodal AI systems by integrating text, image, and audio data. The course demonstrates how combining different data modes such as text, audio, and images can help AI systems achieve significant and advanced capabilities. Participants in this course will gain independent hands-on experience in key areas including building visual question-answering models, generating personalized imagery using diffusion models, designing end-to-end multimodal applications, and fine-tuning multimodal models to perform specific tasks. This comprehensive course provides learners with the essential tools, conceptual understanding, and confidence to design and deploy advanced multimodal AI systems. Prerequisites for participating in this course include proficiency in the Python 3 programming language and experience working with interactive environments such as Jupyter Notebook, comfort in using libraries such as Pandas and one of the TensorFlow or PyTorch frameworks, as well as understanding basic machine learning and deep learning concepts, including partitioning data into training and test sets, familiarity with cost functions, and understanding the principles of gradient descent.
What you will learn
- Concepts and applications of multi-faceted artificial intelligence:
- How to apply multifaceted artificial intelligence concepts.
- Building a Voice-to-Voice App.
- Applying the concepts and architecture of Visual Question Answering (VQA).
- Understand the transformative potential of multi-faceted AI systems and their impacts across various industries.
- Production and fine-tuning models:
- Build, fine-tune, and evaluate diffusion models using DreamBooth.
- Fine-tuning a Text-to-Speech Model using SpeechT5.
- Building Visual Agents from Scratch.
- Systems design and evaluation:
- Design and implementation of multi-faceted artificial intelligence applications.
- Evaluating the performance of multimodal models using evaluation criteria, benchmarks, and ethical considerations.
- Developing multi-modal systems with advanced techniques such as the use of computers.
- Understand how to embed and fuse different aspects effectively.
This course is suitable for people who:
- Developers, data scientists, and engineers interested in building intelligent, autonomous, multi-faceted AI systems that are capable of solving complex problems and adapting to dynamic environments.
- People who want to go beyond the basics of multi-faceted AI and create coherent systems that can manage various inputs and outputs.
- Those looking to learn advanced techniques and future trends in this rapidly changing field, such as Generalized AI Agentic Behavior.
Course details
- Publisher: Oreilly
- Instructor: Sinan Ozdemir
- Education level: Intermediate
- Training duration: 5 hours and 33 minutes
Course topics
Course images
Sample course video
Installation Guide
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Subtitles: None
Quality: 720p
Download link
Downloadly
Rapidgator link
File(s) password: www.downloadly.ir
File size
1.9 GB
