I am the author of five books. Three on Deep Learning and neural networks (published by Springer/APRESS) and two on the statistics and the fundamentals of machine learning (published by Springer Nature). In them, I explain all the theory necessary to really understand how the methods used with neural networks work and how to implement them in Python and in particular in Keras (TensorFlow) (in the APRESS Books). In April 2022 the second edition of my book “Applied Deep Learning” was out and was completely updated for TensorFlow 2 and Keras and I added lots of content as autoencoders and GANs.
Examples of Book Reviews from Amazon
Review of Applied Deep Learning with TensorFlow 2.
Review of Advanced Applied Deep learning: Convolutional Neural Networks and Object Detection.
Statistics for Scientists - A Primer for Researchers and Professionals, Springer Briefs Collection, Springer Nature, 2024, (Available in June 2024)
Fundamental Mathematical Concepts for Machine Learning in Science, Springer Nature 2024, Text Book in Computer Science
Applied Deep Learning with TensorFlow 2, APRESS, Springer Nature, 2022 - 100’000 downloads and 180 citations in scientific papers
Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection, APRESS, Springer Nature, 2019 - 50’000 downloads and 140 citations in scientific papers
Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks, APRESS, Springer Nature, 2018 - 70’000 downloads, 180 citations in scientific papers
My books have been
downloaded more than 220’000 times
cited in total by more than 330 international peer reviewed papers
Used in PATENT applications
My books have been cited by papers in a large number of different fields:
medicine
drug testing
geology
market analysis
sales forecasting
construction
physics
water treatment systems
Cosmology
Many more…