- What happens if the learning rate of generator and discriminator models is high?
Empirically, it is observed that the model stability is lower. - In a scenario where the generator and discriminator are very well trained, what is the probability of a given image being real?
0.5. - Why do we use convtranspose2d in generating images?
We cannot upscale/ generate images using a linear layer. - Why do we have embeddings with high embedding size than the number of classes in Conditional GANs?
Using more parameters gives the model more degrees of freedom to learn the important features of each class. - How can we generate images of men that have a beard?
By using a conditional GAN. Just like we had male and female images, we can have bearded males and other such classes while training model. - Why do we have Tanh activation at the last layer in the generator and not ReLU or Sigmoid?
The pixel range of normalized images is [-1,1] and hence we use Tanh - Why did we...
Modern Computer Vision with PyTorch
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Modern Computer Vision with PyTorch
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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.
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Current Title:
Modern Computer Vision with PyTorch
Table of Contents (25 chapters)
Preface
Section 1 - Fundamentals of Deep Learning for Computer Vision
Free Chapter
Artificial Neural Network Fundamentals
PyTorch Fundamentals
Building a Deep Neural Network with PyTorch
Section 2 - Object Classification and Detection
Introducing Convolutional Neural Networks
Transfer Learning for Image Classification
Practical Aspects of Image Classification
Basics of Object Detection
Advanced Object Detection
Image Segmentation
Applications of Object Detection and Segmentation
Section 3 - Image Manipulation
Autoencoders and Image Manipulation
Image Generation Using GANs
Advanced GANs to Manipulate Images
Section 4 - Combining Computer Vision with Other Techniques
Training with Minimal Data Points
Combining Computer Vision and NLP Techniques
Combining Computer Vision and Reinforcement Learning
Moving a Model to Production
Using OpenCV Utilities for Image Analysis
Other Books You May Enjoy
Appendix
Customer Reviews