Deep Learning for Computer Vision with Tensor Flow and Keras

h264, yuv420p, 1280×720 |ENGLISH, aac, 48000 Hz, 2 channels | 8h 51 mn | 5.07 GB
Created by: CARLOS QUIROS

Deep Learning review, CNNs, VGG-16, Faster R-CNN, TensorFlow Object Detection API, YOLO. Train your own data.

What you’ll learn
Learn about the state of the art in object detection and image classification models.

Requirements
Machine Learning concepts, Linear Algebra, Python, Tensor Flow, Keras and OpenCV
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 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 different robust architectures.

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.
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.

Who this course is for:
Professionals who wants to learn advanced applications on Computer Vision using deep learning concepts.

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