Vision

O’Reilly – Applied Computer Vision with Python Video Course

O’Reilly – Applied Computer Vision with Python Video Course
English | Size: 2.73 GB
Category: CBTs


Get started with Applied Computer Vision in Python. Topics include: 1. Introduction to applied computer vision 2. Emerging Topics in applied computer vision 3. Using AI APIs 4. Using AutoML for Computer vision 5. Using Edge Computer Vision Hardware 6. Using AWS for Computer Vision with AWS DeepLens and AWS Lambda

Packt – Computer Vision: Face Recognition Quick Starter in Python

Packt – Computer Vision Face Recognition Quick Starter in Python-RiDWARE
English | Size: 1.92 GB
Category: Tutorial


Face detection and face recognition are the most popular aspects in computer vision. They are widely used by governments and organizations for surveillance and policing. Moreover, they also have applications in our day-to-day life such as face unlocking mobile phones.

PyTorch for Deep Learning and Computer Vision

PyTorch for Deep Learning and Computer Vision
English | Size: 5.53 GB
Category: Tutorial


Implement Machine and Deep Learning applications with PyTorch
Build Neural Networks from scratch
Build complex models through the applied theme of Advanced Imagery and Computer Vision
Solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models
Use style transfer to build sophisticated AI applications

Packt – Hands On Computer Vision with OpenCV 4 Keras and TensorFlow 2

[/img]

Packt – Hands On Computer Vision with OpenCV 4 Keras and TensorFlow 2-RiDWARE
English | Size: 1.51 GB
Category: Tutorial


Learn
Image manipulations (dozens of techniques-such as transformations, blurring, thresholding, edge detection, and cropping)
How to segment images using a variety of OpenCV algorithms, from contouring to blob and line detection
Approximate contours and perform contour filtering, ordering, and approximations
Perform object detection for faces, people, and cars
Use Machine Learning in computer vision, including understanding Deep Learning models such as convolutional neural networks
Create a varying range of image classifiers-for example, recognizing handwritten digits, gesture recognition, and other multi-class classifiers
Perform facial recognition with deep learning