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9.5 MB
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CE3591D470C68AE6C56B7EC1726603F32ED07C50
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Oct. 28, 2025, 10:33 a.m.
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(Last updated: Oct. 28, 2025, 10:34 a.m.)
| File | Size |
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| Verma P. Handbook of Deep Learning Models Vol One. Fundamentals 2026.pdf | 9.5 MB |
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DL
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1005.8 KB
[10
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2]
2024-12-20
| Uploaded by zakareya | Size 1005.8 KB | Health [ 10 /2 ] | Added 2024-12-20 |
NOTE
SOURCE: Verma P. Handbook of Deep Learning Models Vol One. Fundamentals 2026
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COVER
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MEDIAINFO
Textbook in PDF format This volume covers a comprehensive range of fundamental concepts in Deep Learning and Artificial Neural Networks, making it suitable for beginners looking to learn the basics. Using Keras, a popular neural network API in Python, this book offers practical examples that reinforce the theoretical concepts discussed. Real-world case studies add relevance by showing how Deep Learning is applied across various domains. The book covers topics such as layers, activation functions, optimization algorithms, backpropagation, convolutional neural networks (CNNs), data augmentation, and transfer learning – providing a solid foundation for building effective neural network models. Utilizing the intuitive and widely used Keras API in Python, we present practical examples that reinforce theoretical concepts. Real-time use case studies further illustrate how Deep Learning is being effectively deployed across domains such as healthcare, finance, computer vision, and natural language processing (NLP). The book is structured to take readers on a journey from the fundamentals of neural networks – including layers, activation functions, and backpropagation – to more advanced concepts like convolutional neural networks (CNNs), transfer learning, optimization algorithms, and data augmentation. The Chapter 1 provides an introduction to the field of Deep Learning, starting with an overview of Artificial Intelligence (AI), Machine Learning, and Deep Learning. It explores the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), by providing an overview of these concepts, explaining how they are interconnected and how DL is a subset of ML, which in turn is a subset of AI. It explains the historical context of DL by tracing the evolution of ML and highlighting the key milestones in the field. The chapter explores what sets DL apart from traditional ML approaches, emphasizing its ability to learn and extract complex patterns and representations directly from raw data. It discusses the modern landscape of ML, including the popularity and widespread adoption of DL in various domains. Moreover, it discusses the advances in related fields that have contributed to the rise of DL, such as computer vision, natural language processing, and speech recognition. The chapter highlights the interconnectedness of these fields and how they have influenced the development of DL techniques. To embark on the journey of DL, the chapter outlines the prerequisites that will help readers grasp the concepts effectively. This includes a basic understanding of linear algebra, calculus, and probability theory. Additionally, the chapter provides guidance on installing the required libraries and frameworks, ensuring readers are ready to dive into hands-on implementation. This book is a valuable resource for anyone interested in Deep Learning and Artificial Neural Networks, offering both theoretical insights and practical implementation experience
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