Modern Computer Vision with PyTorch by V Kishore Ayyadevara, Yeshwanth Reddy
Requirements: .ePUB reader, 93 mb
Overview: Deep learning for computer vision (CV) has had a considerable positive impact on several applications.
First you will learn to implement a neural network (NN) from scratch using both NumPy, PyTorch and then learn the best practices of tweaking a NN’s hyper-parameters.
As we progress, you will learn about CNNs, transfer-learning with a focus on classifying images. You will also learn about the practical aspects to take care of while building a NN model.
Next you will learn about multi-object detection, segmentation and implement them using R-CNN family, SSD, YOLO, U-Net, Mask-RCNN architectures. You will then learn to use Detectron2 framework to simplify the process of building a NN for object detection and human-pose-estimation. Finally you will implement 3-D object detection.
Subsequently, you will learn about auto-encoders and GANs with a strong focus on image manipulation and generation. Here, you will implement VAE, DCGAN, CGAN, Pix2Pix, CycleGan, StyleGAN2, SRGAN, Style-Transfer.
You will then learn to combine NLP and CV techniques while performing OCR, Image Captioning, object detection with transformers. Next, you will learn to combine RL with CV techniques to implement a self-driving car agent.
Finally, you’ll wrap up with moving a NN model to production and learn conventional CV techniques using open-cv library.
Genre: Non-Fiction > Tech & Devices
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