Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
5 (2)
Book Image

Modern Computer Vision with PyTorch

5 (2)
By: V Kishore Ayyadevara, Yeshwanth Reddy

Overview of this book

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.
Table of Contents (25 chapters)
1
Section 1 - Fundamentals of Deep Learning for Computer Vision
5
Section 2 - Object Classification and Detection
13
Section 3 - Image Manipulation
17
Section 4 - Combining Computer Vision with Other Techniques

Chapter 1 - Artificial Neural Network Fundamentals

  1. What are the various layers in a neural network?
    Input, Hidden, and Output Layers
  2. What is the output of a feed-forward propagation?
    Predictions that help in calculating loss value
  3. How is the loss function of a continuous dependent variable different from that of a binary dependent variable and also of a categorical dependent variable?
    MSE is the generally used loss function for a continuous dependent variable and binary cross-entropy for a binary dependent variable. Categorical cross-entropy is used for categorical dependent variables.
  4. What is stochastic gradient descent?
    It is a process of reducing loss, by adjusting weights in the direction of decreasing gradient
  5. What does a backpropagation exercise do?
    It computes gradients of all weights with respect to loss using the chain rule
  6. How does the weight update of all the weights across layers happen during back-propagation?
    It happens using the formula dW = W – alpha*(dW/dL)
  7. What...