Federated and Transfer Learning
Roozbeh Razavi-Far · Boyu Wang · Matthew E. Taylor · Qiang Yang
ሴፕቴ 2022 · Adaptation, Learning, and Optimization መጽሐፍ 27 · Springer Nature
ኢ-መጽሐፍ
371
ገጾች
reportየተሰጡት ደረጃዎች እና ግምገማዎች የተረጋገጡ አይደሉም የበለጠ ለመረዳት
ስለዚህ ኢ-መጽሐፍ
This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
ለዚህ ኢ-መጽሐፍ ደረጃ ይስጡ
ምን እንደሚያስቡ ይንገሩን።
የንባብ መረጃ
ዘመናዊ ስልኮች እና ጡባዊዎች
የGoogle Play መጽሐፍት መተግበሪያውን ለAndroid እና iPad/iPhone ያውርዱ። ከእርስዎ መለያ ጋር በራስሰር ይመሳሰላል እና ባሉበት የትም ቦታ በመስመር ላይ እና ከመስመር ውጭ እንዲያነቡ ያስችልዎታል።
ላፕቶፖች እና ኮምፒውተሮች
የኮምፒውተርዎን ድር አሳሽ ተጠቅመው በGoogle Play ላይ የተገዙ ኦዲዮ መጽሐፍትን ማዳመጥ ይችላሉ።
ኢሪደሮች እና ሌሎች መሳሪያዎች
እንደ Kobo ኢ-አንባቢዎች ባሉ ኢ-ቀለም መሣሪያዎች ላይ ለማንበብ ፋይል አውርደው ወደ መሣሪያዎ ማስተላለፍ ይኖርብዎታል። ፋይሎቹን ወደሚደገፉ ኢ-አንባቢዎች ለማስተላለፍ ዝርዝር የእገዛ ማዕከል መመሪያዎቹን ይከተሉ።
