Deep Learning for Computer Vision with Tensor Flow and Keras

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 using GPU computing with Yolo v2 and Yolo v3 but in Google Colab.
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.