Python Deep Learning - Second Edition
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This book 'Python Deep Learning - Second Edition' by Ivan Vasilev et al. (January 2019) will help in learning advanced state-of-the-art deep learning techniques and their applications using popular Python libraries.
With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects.
What You Will Learn:
1. Grasp the mathematical theory behind neural networks and deep learning processes
2. Investigate and resolve computer vision challenges using convolutional networks and capsule networks
3. Solve generative tasks using variational autoencoders and Generative Adversarial Networks
4. Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
5. Explore reinforcement learning and understand how agents behave in a complex environment
6. Get up to date with applications of deep learning in autonomous vehicles
Author Bio:
Ivan Vasilev
Ivan Vasilev started working on the first open source Java Deep Learning library with GPU support in 2013. The library was acquired by a German company, where he continued its development. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017 he has focused on financial machine learning. He is working on a Python open source algorithmic trading library, which provides the infrastructure to experiment with different ML algorithms. The author holds an MSc degree in Artificial Intelligence from The University of Sofia, St. Kliment Ohridski.
Daniel Slater
Daniel Slater started programming at age 11, developing mods for the id Software game Quake. His obsession led him to become a developer working in the gaming industry on the hit computer game series Championship Manager. He then moved into finance, working on risk- and high-performance messaging systems. He now is a staff engineer working on big data at Skimlinks to understand online user behavior. He spends his spare time training AI to beat computer games. He talks at tech conferences about deep learning and reinforcement learning; and the name of his blog is Daniel Slater's blog. His work in this field has been cited by Google.
Gianmario Spacagna
Gianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for internet of things (IoT) and connected-vehicle applications. He works closely with tire mechanics, engineers, and business units to analyze and formulate hybrid, physics-driven, and data-driven automotive models. His main expertise is in building ML systems and end-to-end solutions for data products. He holds a master's degree in telematics from the Polytechnic of Turin, as well as one in software engineering of distributed systems from KTH, Stockholm. Prior to Pirelli, he worked in retail and business banking (Barclays), cyber security (Cisco), predictive marketing (AgilOne), and did some occasional freelancing.
Peter Roelants
Peter Roelants holds a master's in computer science with a specialization in AI from KU Leuven. He works on applying deep learning to a variety of problems, such as spectral imaging, speech recognition, text understanding, and document information extraction. He currently works at Onfido as a team leader for the data extraction research team, focusing on data extraction from official documents.
Valentino Zocca
Valentino Zocca has a PhD degree and graduated with a Laurea in mathematics from the University of Maryland, USA, and University of Rome, respectively, and spent a semester at the University of Warwick. He started working on high-tech projects of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. There he developed many mathematical algorithms and predictive models, and using Hadoop he automated several satellite-imagery visualization programs. He has worked as an independent consultant at the U.S. Census Bureau, in the USA and in Italy. Currently, Valentino lives in New York and works as an independent consultant to a large financial company.
This book 'Python Deep Learning - Second Edition' by Ivan Vasilev et al. (January 2019) will help in learning advanced state-of-the-art deep learning techniques and their applications using popular Python libraries.
With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects.
What You Will Learn:
1. Grasp the mathematical theory behind neural networks and deep learning processes
2. Investigate and resolve computer vision challenges using convolutional networks and capsule networks
3. Solve generative tasks using variational autoencoders and Generative Adversarial Networks
4. Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
5. Explore reinforcement learning and understand how agents behave in a complex environment
6. Get up to date with applications of deep learning in autonomous vehicles
Author Bio:
Ivan Vasilev
Ivan Vasilev started working on the first open source Java Deep Learning library with GPU support in 2013. The library was acquired by a German company, where he continued its development. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017 he has focused on financial machine learning. He is working on a Python open source algorithmic trading library, which provides the infrastructure to experiment with different ML algorithms. The author holds an MSc degree in Artificial Intelligence from The University of Sofia, St. Kliment Ohridski.
Daniel Slater
Daniel Slater started programming at age 11, developing mods for the id Software game Quake. His obsession led him to become a developer working in the gaming industry on the hit computer game series Championship Manager. He then moved into finance, working on risk- and high-performance messaging systems. He now is a staff engineer working on big data at Skimlinks to understand online user behavior. He spends his spare time training AI to beat computer games. He talks at tech conferences about deep learning and reinforcement learning; and the name of his blog is Daniel Slater's blog. His work in this field has been cited by Google.
Gianmario Spacagna
Gianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for internet of things (IoT) and connected-vehicle applications. He works closely with tire mechanics, engineers, and business units to analyze and formulate hybrid, physics-driven, and data-driven automotive models. His main expertise is in building ML systems and end-to-end solutions for data products. He holds a master's degree in telematics from the Polytechnic of Turin, as well as one in software engineering of distributed systems from KTH, Stockholm. Prior to Pirelli, he worked in retail and business banking (Barclays), cyber security (Cisco), predictive marketing (AgilOne), and did some occasional freelancing.
Peter Roelants
Peter Roelants holds a master's in computer science with a specialization in AI from KU Leuven. He works on applying deep learning to a variety of problems, such as spectral imaging, speech recognition, text understanding, and document information extraction. He currently works at Onfido as a team leader for the data extraction research team, focusing on data extraction from official documents.
Valentino Zocca
Valentino Zocca has a PhD degree and graduated with a Laurea in mathematics from the University of Maryland, USA, and University of Rome, respectively, and spent a semester at the University of Warwick. He started working on high-tech projects of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. There he developed many mathematical algorithms and predictive models, and using Hadoop he automated several satellite-imagery visualization programs. He has worked as an independent consultant at the U.S. Census Bureau, in the USA and in Italy. Currently, Valentino lives in New York and works as an independent consultant to a large financial company.