Introduction to Deep Learning Business Applications for Developers: From Conversational Bots in Customer Service to Medical Image Processing
Armando Vieira ยท Bernardete Ribeiro
เบ.เบ. 2018 ยท Apress
เบเบถเปเบกเบญเบตเบเบธเบ
343
เปเปเบฒ
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Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles.
An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. Youโll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer.
After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework.
What You Will Learn
Who This Book Is For Data scientists, entrepreneurs, and business developers.
An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. Youโll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer.
After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework.
What You Will Learn
- Find out about deep learning and why it is so powerful
- Work with the major algorithms available to train deep learning models
- See the major breakthroughs in terms of applications of deep learning
- Run simple examples with a selection of deep learning libraries
- Discover the areas of impact of deep learning in business
Who This Book Is For Data scientists, entrepreneurs, and business developers.
เบเปเบฝเบงเบเบฑเบเบเบนเปเบเบฝเบ
Dr Armando Vieira is a Data Scientist and Artificial Intelligence consultant with an entrepreneurial mindset. Passionate about how to make Machine Learning projects work for organizations and how to build great AI based products.As algorithms are becoming a commodity, the challenge is not building them but using them to solve real problems.
Have coordinated several projects on Credit Risk Evaluation, Recommendation Systems, Clustering Analysis and Predictive Analytics.
Have coordinated several projects on Credit Risk Evaluation, Recommendation Systems, Clustering Analysis and Predictive Analytics.
Bernardete Ribeiro is Professor at University of Coimbra, Portugal. She has a Ph.D. and Habilitation in Informatics Engineering. She is Director of the Center of Informatics and Systems of the University of Coimbra (CISUC).She is President of the Portuguese Association of Pattern Recognition (APRP). She is Founder and Director of the Laboratory of Artificial Neural Networks (LARN) for more than 20 years. She is IEEE SMC Senior member, member of International Association of Pattern Recognition (IAPR), International Neural Network Society (INNS), and ACM. Her research interests are in the areas of Machine Learning, Pattern Recognition, and their applications to abroad range of fields. She is author or co-author of over three hundred publications including books, journalsand international and national conferences. She has delivered numerous invited talks, seminars, and short courses.
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