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Generative AI with Python and TensorFlow: A Complete Guide to Mastering AI Models

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Book Details
Language
English
Publishers
Independently published (3 July 2024)
Weight
0.14 KG
Publication Date
03/07/2024
Pages
88 pages
ISBN-13
9798332106040
Dimensions
15.24 x 0.51 x 22.86 cm
SKU
9798332106040
Author Name
Anand Vemula (Author)
Anand Vemula is a technology, business, ESG and Risk governance Evangelist. He has more than 27 plus years of experience. Has worked in MNC at a CXO level. Has been a part of various projects and forums across customers in BFSI, Healthcare, Retail, Manufacuring, Lifesciences, Energy Industry Verticals. Certified in all the technologies and Enterprise Digital ArchitectRead more about this authorRead less about this author
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Generative AI with Python and TensorFlow: A Complete Guide to Mastering AI Models is a comprehensive resource for anyone looking to delve into the world of generative artificial intelligence. IntroductionOverview of Generative AI: Understand the basic concepts, history, and significance of generative AI.

Importance of Generative AI: Learn about the transformative potential of generative AI in various industries. Applications and Use Cases: Explore real-world applications of generative AI in fields such as art, music, text generation, and data augmentation.

Overview of Python and TensorFlow: Get an introduction to the essential tools and libraries used for building generative AI models. Getting Started: Set up your development environment, install necessary libraries, and take your first steps with TensorFlow.

Fundamentals of Machine LearningSupervised vs. Unsupervised Learning: Understand the differences and use cases of these two primary types of machine learning.

Neural Networks Basics: Learn the fundamental concepts of neural networks and their role in AI. Introduction to Deep Learning: Dive deeper into the advanced techniques of deep learning and its applications in generative AI.

Key Concepts in Generative AI: Familiarize yourself with the essential concepts and terminologies in generative AI. Generative ModelsUnderstanding Generative Models: Explore the theoretical foundations of generative models.

Types of Generative Models: Learn about various types of generative models, including VAEs, GANs, autoregressive models, and flow-based models. Variational Autoencoders (VAEs): Delve into the theory behind VAEs, build and train VAEs with TensorFlow, and explore their use cases.

Generative Adversarial Networks (GANs): Get introduced to GANs, understand their architecture, implement GANs with TensorFlow, and learn advanced GAN techniques. Autoregressive Models: Understand autoregressive models, implement them with TensorFlow, and explore their applications.

Flow-based Models: Learn about flow-based models, build them with TensorFlow, and explore their practical applications. Advanced TopicsTransfer Learning for Generative Models: Explore how transfer learning can be applied to generative models.

Conditional Generative Models: Understand and implement models that generate outputs conditioned on specific inputs. Multimodal Generative Models: Learn about models that can generate multiple types of data simultaneously.

Reinforcement Learning in Generative AI: Explore the intersection of reinforcement learning and generative AI. Practical ApplicationsImage Generation and Style Transfer: Create stunning images and apply style transfer techniques.

Text Generation and Natural Language Processing: Generate coherent and contextually relevant text using advanced NLP techniques. Music and Sound Generation: Compose music and generate new sounds using generative AI.

Data Augmentation for Machine Learning: Improve your machine learning models by augmenting your datasets with generative models. Hands-On ProjectsProject 1: Creating Art with GANs: Step-by-step guide to building a GAN to generate art.

Project 2: Text Generation with LSTM: Implement an LSTM model for generating text. Project 3: Building a VAE for Image Reconstruction: Learn how to build and train a VAE for image reconstruction.

Project 4: Music Generation with RNNs: Create a music generation model using RNNs. .

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