Updated on 9/9/2018: After having learnt basic concepts and played with many hands-on examples given in this text, readers are encouraged to study Appendix C thoroughly, from which, you will learn YOLOv3 - one of the state of the art deep learning frameworks open sourced. The author shares with you how he has optimized YOLOv3 to run 10 to 20 times faster on macOS after replacing the original gemm ...
Paperback: 481 pages
Publisher: CreateSpace Independent Publishing Platform (March 12, 2018)
Product Dimensions: 7.5 x 1.1 x 9.2 inches
Amazon Rank: 2032311
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This book is an excellent source of knowledge. I had only heard of machine learning in the past but never had good understanding of how it works. This book helped me getting a detailed view of machine learning from it’s early days to its status quo m...
n YOLOv3 with the cblass_sgemm functions from Apple's Accelerate framework. In case you do not have access to GPUs, this appendix demonstrates how you can run YOLOv3 optimized on macOS with a far more larger scale dataset of COCO to bridge the gap between CPUs and GPUs for deep learning training. This appendix also added detailed instructions on how you can create a Ubuntu VM on your macOS machine that does not have a GPU installed, and then compile YOLOv3 with NVidia cuda-9.1 so that you can run your YOLOv3 binary with COCO on a remote machine instance equipped with GPUs.