Deep Learning for Computer Vision with Tensor Flow and Keras
- 3.8
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Brief Introduction
ConvNets, VGG-16, ResNet, Inception, Faster R-CNN, TensorFlow Object Detection, YOLO v2-v3-v4. Train your own data.Description
This course is focused in the application of Deep Learning for image classification and object detection. This course includes a review of the main lbraries for Deep Learning such as Tensor Flow 1.X (not 2.x) and Keras, the combined application of them with OpenCV and also covers a concise review of the main concepts in Deep Learning. We will also enter in the study of Convolutional Neural Networks for image classification reviewing its principal components and different robust architectures such as VGG-16, ResNet and Inception.
We will explore the concepts of Object Detecting and Transfer Learning using the last state of the art algorithms for object detection such as Faster R-CNN, TensorFlow Object Detection API and YOLO, applying this models on images, videos, and webcam images.
Finally you will learn how to construct and train your own dataset through GPU computing running Yolo v2, Yolo v3 and the latest Yolo v4 using Google Colaboratory.
You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples.
Requirements
- Requirements
- Machine Learning concepts, Linear Algebra, Python, Tensor Flow, Keras and OpenCV
CARLOS QUIROS
3.8 Raiting
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>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. The Specialization will be ready late spring, early summer 2018...
