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    Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh
    Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh - okladka książki

    Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh - okladka książki

    Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh - audiobook MP3

    Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras Dipanjan Sarkar, Raghav Bali, Tamoghna Ghosh - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
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    Stron:
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    Do przechowalni

    Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

    The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.

    The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).

    By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.

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    O autorach książki

    Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.
    Raghav Bali has a master's degree (gold medalist) in information technology from International Institute of Information Technology, Bangalore. He is a data scientist at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development to develop scalable machine learning-based solutions. He has worked as an analyst and developer in domains such as ERP, finance, and BI with some of the top companies of the world.
    Tamoghna Ghosh is a machine learning engineer at Intel Corporation. He has overall 11 years of work experience including 4 years of core research experience at Microsoft Research (MSR) India. At MSR he worked as a research assistant in cryptanalysis of block ciphers. His technical expertise's are in big data, machine learning, NLP, information retrieval, data visualization and software development. He received M.Tech (Computer Science) degree from the Indian Statistical Institute, Kolkata and M.Sc (Mathematics) from University of Calcutta with specialization in functional analysis and mathematical modeling/dynamical systems. He is passionate about teaching and conducts internal training in data science for Intel at various levels.

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