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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.

By: Material type: TextTextPublisher: New Delhi : PB Publications, 2021Edition: 1st editionDescription: 208 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9789390684687
Subject(s): DDC classification:
  • 23 006.3 S.B.E
Contents:
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.
Summary: 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]{index=1}
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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Books Books Media and mass communication Library B4 006.3 S.B.E C.1 Available MA0002506
Total holds: 0

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]{index=1}

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