Elements of deep learning for computer vision : explore deep neural network architectures, PyTorch, object detection algorithms, and computer vision applications for Python coders /
Sikka, Bharat,
Elements of deep learning for computer vision : explore deep neural network architectures, PyTorch, object detection algorithms, and computer vision applications for Python coders / Bharat Sikka. - 1st edition. - 208 pages : illustrations ; 24 cm.
1. 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.
Provides 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]
9789390684687
Neural networks$xProgramming.
Deep learning.
006.3 / S.B.E
Elements of deep learning for computer vision : explore deep neural network architectures, PyTorch, object detection algorithms, and computer vision applications for Python coders / Bharat Sikka. - 1st edition. - 208 pages : illustrations ; 24 cm.
1. An Introduction to Deep Learning ‡ 2. Supervised Learning ‡ 3. Gradient Descent ‡ 4. OpenCV with Python ‡ 5. Python Imaging Library and Pillow ‡ 6. Introduction to Convolutional Neural Networks ‡ 7. GoogLeNet, VGGNet, and ResNet ‡ 8. Understanding Object Detection ‡ 9. Popular Algorithms for Object Detection ‡ 10. Faster R‑CNN with PyTorch and YoloV4 with Darknet ‡ 11. Comparing Algorithms and API Deployment with Flask ‡ 12. Applications in Real World.
Provides a thorough conceptual and practical introduction to deep learning in computer vision. Covers PyTorch-based neural network implementation, OpenCV/Pillow image handling, major CNN architectures (GoogLeNet, VGG, ResNet), object detection models (Faster R‑CNN, YOLOv4), and deployment via APIs. Ideal for developers seeking applied knowledge. :contentReference[oaicite:1]
9789390684687
Neural networks$xProgramming.
Deep learning.
006.3 / S.B.E