Generative Deep Learning with Python : Unleashing the Creative Power of AI by Mastering AI and Python
Enregistré dans:
| Autres auteurs: | |
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| Support: | E-Book |
| Langue: | Anglais |
| Publié: |
Birmingham :
Packt Publishing.
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| Autres localisations: | Voir dans le Sudoc |
| Résumé: | Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications.Key FeaturesComprehensive coverage of deep learning and generative models.In-depth exploration of GANs, VAEs, & autoregressive models & advanced topics in generative AI.Practical coding exercises & interactive assignments to build your own generative models.Book DescriptionGenerative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation. The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field. Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.What you will learnDevelop a detailed understanding of deep learning fundamentalsImplement and train Generative Adversarial Networks (GANs)Create & utilize Variational Autoencoders for data generationApply autoregressive models for text generationExplore advanced topics & stay ahead in the field of generative AIAnalyze and optimize the performance of generative modelsWho this book is forThis course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial |
| Accès en ligne: | Accès à l'E-book |
| Résumé: | Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications.Key FeaturesComprehensive coverage of deep learning and generative models.In-depth exploration of GANs, VAEs, & autoregressive models & advanced topics in generative AI.Practical coding exercises & interactive assignments to build your own generative models.Book DescriptionGenerative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation. The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field. Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.What you will learnDevelop a detailed understanding of deep learning fundamentalsImplement and train Generative Adversarial Networks (GANs)Create & utilize Variational Autoencoders for data generationApply autoregressive models for text generationExplore advanced topics & stay ahead in the field of generative AIAnalyze and optimize the performance of generative modelsWho this book is forThis course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial |
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| Description: | Couverture (https://static2.cyberlibris.com/books_upload/136pix/9781836207122.jpg). |
| ISBN: | 9781836207122 |
| Accès: | L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement |