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Neural Networks LLM & Gen AI - Mastering Neural Networks Building Your Own Large Language Model and Exploring Generative AI - cover
ÉCOUTER EXTRAIT

Neural Networks LLM & Gen AI - Mastering Neural Networks Building Your Own Large Language Model and Exploring Generative AI

Et Tu Code

Narrateur Helen Green

Maison d'édition: Et Tu Code

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Synopsis

Unlock the power of artificial intelligence with this comprehensive guide to neural networks, large language models, and generative AI. 
Are you interested in harnessing the power of artificial intelligence to revolutionize your field or industry?  
Starting with the basics, you'll learn how to:  
* Understand the fundamentals of neural networks and their applications in various fields  
* Design and train your own neural network models using popular frameworks like TensorFlow and PyTorch  
* Build a large language model that can generate coherent and contextually relevant text  
* Explore generative AI techniques, including GANs and VAEs, to create innovative solutions for a variety of industries  
But "Neural Networks, LLM & Gen AI" is more than just a technical guide. It's also a comprehensive resource for understanding the ethical considerations and potential pitfalls of working with advanced AI technologies.  
You'll learn how to:  
* Evaluate the bias and fairness of your AI models  
* Mitigate the risks associated with AI development, such as privacy concerns and job displacement  
* Navigate the legal and regulatory landscape of AI, including emerging regulations like the European Union's General Data Protection Regulation (GDPR)  
Whether you're a seasoned developer or just starting out in the world of AI, "Neural Networks, LLM & Gen AI" has something for everyone.  
With its comprehensive coverage and practical examples, this book is the perfect resource for anyone looking to harness the power of artificial intelligence to drive innovation and progress.  
So why wait? Dive into the exciting world of neural networks, large language models, and generative AI today!
Durée: environ 9 heures (08:52:07)
Date de publication: 13/10/2024; Unabridged; Copyright Year: — Copyright Statment: —