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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing How to use synthetic data to save labeling costs for training How do we increase robustness and decrease memory usage For inevitably poor conditions: How do we know that the network is uncertain about its decisions Can we understand a bit more about what actually happens inside neural networks This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Taschenbuch. Zustand: Neu. Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks | A Deep Learning approach for forecasting stock price time-series data in groups | Armin Lawi (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204190921 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Deep Neural Networks and Data for Automated Driving | Robustness, Uncertainty Quantification, and Insights Towards Safety | Jeonghoon Mo | Taschenbuch | X | Englisch | 2022 | Springer Nature Switzerland | EAN 9783031012358 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: Gut. Zustand: Gut | Seiten: 448 | Sprache: Englisch | Produktart: Bücher | This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety?This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and,last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing How to use synthetic data to save labeling costs for training How do we increase robustness and decrease memory usage For inevitably poor conditions: How do we know that the network is uncertain about its decisions Can we understand a bit more about what actually happens inside neural networks This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and,last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Taschenbuch. Zustand: Neu. Beginning Deep Learning with Tensorflow | Work with Keras, Mnist Data Sets, and Advanced Neural Networks | Liangqu Long (u. a.) | Taschenbuch | xxiii | Englisch | 2022 | Apress | EAN 9781484279144 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 448 | Sprache: Englisch | Produktart: Bücher | This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety?This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 713 | Sprache: Englisch | Produktart: Bücher | Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. Yoüll start with an introduction to AI, where yoüll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, yoüll jump into simple classification programs for hand-writing analysis. Once yoüve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, yoüll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! What You'll LearnDevelop using deep learning algorithmsBuild deep learning models using TensorFlow 2Create classification systems and other, practical deep learning applicationsWho This Book Is ForStudents, programmers, and researchers with no experience in deep learning who want to build up their basic skillsets. Experienced machine learning programmers and engineers might also find value in updating their skills.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | I wrote this book as I got an interview offer for Data Analyst. There they asked me a lot of questions and there was an exam. This helped me a lot to write the book based on the interview questions faced by me and the knowledge gained by working on AI projects. I then added all my other knowledge working as a Data Analyst on my other projects, and wrote the book. Technical books need a lot of attention, as they need deep checks, but I tried to do my best. Not everything can be included in detail, it is impossible. I have tried to include everything related to Data Science that is presently going on in the industry and the world.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.