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Generative Ai For Developers: A Practical Implementation - AD-TEAM - 12-07-2024 Generative Ai For Developers: A Practical Implementation Published 11/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.37 GB | Duration: 2h 7m From Theory to Practice: Developing with Generative AI What you'll learn Understand Generative AI Fundamentals: Students will gain a solid understanding of the principles and techniques behind generative AI, including various models Implement Generative AI Models: Students will learn how to implement and customize generative AI models using popular frameworks such as TensorFlow and PyTorch Evaluate and Optimize AI Models: Students will develop skills in evaluating the performance of generative AI models through metrics such as fidelity, diversity Deploy Generative AI Solutions: Students will acquire practical knowledge on how to deploy generative AI models into production environments Requirements No prior experience with generative AI is required. The course is designed to guide students through the learning process, providing all the necessary information and resources to succeed, from foundational concepts to advanced implementation techniques. Description Unlock the power of Generative AI and elevate your development skills with this comprehensive course designed for developers and data scientists. Generative AI for Developers: A Practical Implementation provides hands-on training to help you master the latest techniques in machine learning, deep learning, and artificial intelligence.In this course, you will:Explore Generative AI models such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).Learn how to implement AI solutions that can generate realistic images, text, and other data formats.Gain practical experience with popular libraries like Transformers.Understand the ethical considerations and best practices for using Generative AI in your projects.Work on real-world case studies and projects to build your portfolio, demonstrating your ability to deploy AI models in a professional environment.Whether you're looking to enhance your resume, stay ahead in the rapidly evolving field of technology, or simply explore the fascinating world of Generative AI, this course has everything you need. By the end of this course, you will be equipped with the knowledge and skills to create innovative AI applications, implement cutting-edge machine learning techniques, and contribute to the next generation of intelligent systems.Enroll now and start your journey towards becoming a proficient Generative AI developer! Overview Section 1: Introduction Lecture 1 Welcome & Course Overview Lecture 2 Introduction to Generative AI Section 2: Understanding Generative Models: Concepts and Techniques Lecture 3 Understanding Models: An Overview Lecture 4 What are Generative Models? Lecture 5 Exploring the Diversity of Generative Models Section 3: Generative Adversarial Networks (GANs) Lecture 6 Introduction to GANs (Generative Adversarial Networks) Lecture 7 GAN Components and Variants Lecture 8 Demo-Generator and discriminator Lecture 9 Challenges, Ethical Considerations, and Future Trends Section 4: Variational Autoencoders (VAEs) Lecture 10 Introduction to VAE Variants (CVAE, VQ-VAE) Lecture 11 Demo:Variational Autoencoder (VAE) for MNIST Digit Reconstruction Section 5: Autoencoders Lecture 12 Introducing Autoencoders Lecture 13 Autoencoder-based Image Compression and Denoising Demo Section 6: Large Language Models in Generative AI Lecture 14 Introducing Large Language Models (LLMs) Lecture 15 Decoding the Architecture of LLMs Section 7: Transformer-Based Generative Models Lecture 16 Transformer-based generative models Lecture 17 Demo-Transformer Based Translator Lecture 18 Demo-Transformer Based Sentiment Analysis Lecture 19 Demo Creative Content Generation with Generative AI Lecture 20 Demo-Transformer Based Text Generation Section 8: Practice Test This course is tailored for those who want to gain practical, actionable knowledge in generative AI, regardless of their previous experience level in the field. |