000			01853nam a22002657i 4500
005			20250819205612.0
008			250722s |||ao||fr|||| 00| 0 eng d
020			_a9789390684687
040			_aEG-GaU‬‬ _cEG-GaU‬‬ _dEG-GaU‬‬ _erda
082	0	4	_223 _a006.3 _bS.B.E
100	1		_aSikka, Bharat, _eauthor. _960457
245	1	0	_aElements of deep learning for computer vision : _bexplore deep neural network architectures, PyTorch, object detection algorithms, and computer vision applications for Python coders / _cBharat Sikka.
250			_a 1st edition.
264		1	_aNew Delhi : _bPB Publications, _c2021.
300			_a208 pages : _billustrations ; _c24 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
505	0		_a1. An Introduction to Deep Learning ‡ 2. Supervised Learning ‡ 3. Gradient Descent ‡ 4. OpenCV with Python ‡ 5. Python Imaging Library and Pillow ‡ 6. Introduction to Convolutional Neural Networks ‡ 7. GoogLeNet, VGGNet, and ResNet ‡ 8. Understanding Object Detection ‡ 9. Popular Algorithms for Object Detection ‡ 10. Faster R‑CNN with PyTorch and YoloV4 with Darknet ‡ 11. Comparing Algorithms and API Deployment with Flask ‡ 12. Applications in Real World.
520			_aProvides a thorough conceptual and practical introduction to deep learning in computer vision. Covers PyTorch-based neural network implementation, OpenCV/Pillow image handling, major CNN architectures (GoogLeNet, VGG, ResNet), object detection models (Faster R‑CNN, YOLOv4), and deployment via APIs. Ideal for developers seeking applied knowledge. :contentReference[oaicite:1]{index=1}
650		0	_aNeural networks$xProgramming. _960458
650		0	_aDeep learning. _960437
942			_2ddc _cBK
999			_c11078 _d11078