{"id":18,"date":"2021-03-08T03:17:56","date_gmt":"2021-03-08T03:17:56","guid":{"rendered":"http:\/\/drssridhar.com\/home\/"},"modified":"2025-08-25T11:31:30","modified_gmt":"2025-08-25T11:31:30","slug":"home","status":"publish","type":"page","link":"https:\/\/www.drssridhar.com\/","title":{"rendered":"Home"},"content":{"rendered":"\n
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Books<\/h2>\n\n\n\n
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Digital Image Processing 2ed<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Design and Analysis of Algorithms, 2ed<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Machine Learning<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Python Programming<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Deep Learning<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Problem Solving and Programming in C<\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n\n
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Design and Analysis Algorithms<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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Digital Image Processing 1ed<\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n\n
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Deep Learning 1 Ed <\/h1>\n\n\n\n
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Deep Learning<\/strong> is designed as a textbook for undergraduate and postgraduate students, providing a strong foundation in deep learning concepts. The book begins with fundamental topics<\/strong> such as artificial intelligence, machine learning, natural language processing, image processing, and computer vision,<\/strong> which are essential for understanding deep learning<\/strong> technologies<\/strong>. Core deep learning concepts, including neural networks, activation functions, loss functions, optimization, and regularization, are explored in depth. Additionally, the book introduces data fundamentals, ensuring a complete learning experience.<\/p>\n\n\n\n

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The book covers major deep learning architectures<\/strong>, 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<\/strong> are presented alongside transfer learning applications. The concepts of autoencoders and Recurrent Neural Networks (RNNs)<\/strong>, 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).<\/strong> 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.<\/strong><\/p>\n\n\n\n

Features \u2013<\/strong><\/p>\n\n\n\n

\ud83d\udcda Deep learning<\/strong> concepts are presented in a clear, concise, and approachable manner, making complex topics easy to understand.<\/p>\n\n\n\n

\ud83d\udd0d Hands-on Learning with an online Keras lab manual, enabling practical implementation of deep learning algorithms.<\/strong><\/p>\n\n\n\n

\ud83c\udf1f Extensive solved numerical problems, providing clarity and reinforcing deep learning concepts.<\/p>\n\n\n\n

\ud83c\udfaf Comprehensive learning support<\/strong>, including summaries, glossaries, conceptual questions, numerical problems, and multiple-choice questions.<\/p>\n\n\n\n

\ud83d\udca1 Engaging pedagogical techniques<\/strong>, such as crossword puzzles and jumbled words, to reinforce key concepts.<\/p>\n\n\n\n

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Buy Now<\/a><\/div>\n<\/div>\n\n\n\n
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Design and Analysis of Algorithm 2Ed is Published!!!<\/h2>\n\n\n\n
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Key Features<\/h2>\n\n\n\n