[BOOK] Artificial neural network and deep learning: fundamentals and theory

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Hi,

I am reading your another book titled “Neural network and deep learning with Mathematica” and found it very useful, especially the chapters 4 and 5. Mathematica has good documentation for individual layers but I found it is difficult to put various layers and functions together such as specifying inputs and outputs to an entire network as well as individual layer, loss functions for discrete and continuous values, use of NetPort, etc. The chapters 4 and 5 provide good examples on how to put all commands together.

Please, in the future, provide more examples about other layers such as FunctionLayer, RecurrentLayer, AttentionLayer, NetMapOperator, etc .

Thanks for your great work.

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Hi Dr. Sangdon Lee,

Thank you so much for your kind words and feedback on “Neural Network and Deep Learning with Mathematica”! I’m thrilled to hear that Chapters 4 and 5 have been helpful to you in navigating the complexities of integrating different layers and functions within Mathematica.

I appreciate your suggestion regarding providing more examples on other layers such as FunctionLayer, RecurrentLayer, AttentionLayer, and NetMapOperator. I’ll definitely take that into consideration for future updates and additions to the book. Ensuring comprehensive coverage of these advanced functionalities is crucial for a deeper understanding and practical application of neural networks.

I’m currently working on a new volume titled “Advanced Topics,” which will include sections on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Large Language Models (LLMs). This new volume will delve into these advanced areas in greater detail, and your feedback will be invaluable in shaping its content.

Best regards,
M. Hammad

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