000			01934nam a22001937a 4500
005			20251201160350.0
020			_a978-9367138663
082			_a006.313 SRI
100			_aSridhar S _9461
100			_aNarashiman D _91142
245			_aDeep Learning
250			_a1
260			_aPearson Education _bNew Delhi _c2025
300			_a696 _bItem Weight ‏ : ‎ 829 g Dimensions ‏ : ‎ 23.5 x 17.2 x 2.7 cm
520			_aThe book covers major deep learning architectures, including Convolutional Neural Networks (CNNs) and Object Detection Networks, with discussions on R-CNN family algorithms, YOLO networks and image segmentation networks. Advanced CNN architectures such as AlexNet, VGGNet, InceptionNet, and ResNet are presented alongside transfer learning applications. The concepts of autoencoders and Recurrent Neural Networks (RNNs), including LSTMs and GRUs, are also introduced. Beyond CNNs, the book also explores Generative AI, covering Large Language Models (LLMs) such as ChatGPT and Generative Adversarial Networks (GANs). It introduces advanced topics like Transformer architectures, along with dedicated chapters on Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), and Deep Reinforcement Learning algorithms. Features – 📚 Deep learning concepts are presented in a clear, concise, and approachable manner, making complex topics easy to understand. 🔍 Hands-on Learning with an online Keras lab manual, enabling practical implementation of deep learning algorithms. 🌟 Extensive solved numerical problems, providing clarity and reinforcing deep learning concepts. 🎯 Comprehensive learning support, including summaries, glossaries, conceptual questions, numerical problems, and multiple-choice questions. 💡 Engaging pedagogical techniques, such as crossword puzzles and jumbled words, to reinforce key concepts.
650			_aCSE _914
942			_cBK
999			_c38056 _d38056