This course is focused in the application of Deep Learning for image classification and object detection. This course originally was designed in TensorFlow version 1.X but now all codes were updated with TensorFlow version 2.X, mainly by the use of Google Colaboratory(Colab).
If you dont have an available GPU in your local system or you want to experiment in an environment without any previous installation or setup, dont worry you can follow the course smootly because all codes were optimized in Google Colab.
The course starts with a concise review of the main concepts in Deep Learning, because this course focused in the application of Deep Learning in the computer vision field.
The main computer vision tasks covered in this course are image classification and object detection.
After reviewing the deep learning theory you will enter in the study of Convolutional Neural Networks (ConvNets) for image classification studying the following concepts and algorithms:
- Image Fundamentals
- Loading images in TensorFlow
- The building blocks of ConvNets such as:
Image Augmentation, etc
- Different ConvNets architectures such as:
- Many practical applications using famous datasets such as:
Covid19 on X-Ray images,
Open Images Dataset V6 through Voxel FiftyOne,
You will also learn how to work and collect image data through web scraping with
Python and Selenium.
Finally in the Object Detection chapter we will explore the theory and the application using Transfer Learning approach using the lastest state of the art algorithms with practical applications. Some of the content in this Chapter is the following:
- Theoretical background for Selective Search algorith,
- Theoretical background for R-CNN, Fast R-CNN and Faster R-CNN,
- Faster R-CNN application on BCCD dataset for detecting blood cells,
- Theoretical background for Single Shot Detector (SSD),
- Training your customs datasets using different models with TensorFlow Object Detection API
- Object Detection on images, videos and livestreaming,
- YOLOv2 theory and practical application in a custom dataset (R2D2 dataset)
- YOLOv3 practical application in a custom dataset (R2D2 and C3PO dataset)
- YOLOv4 theory and practical application in a custom dataset (R2D2 and C3PO dataset)
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 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 using your own datasets.
This course is very well qualified by the students, some of the inspiring comments are:
* Stefan Lankester (5 stars): Thanks Carlos for this valuable training. Good explanation with broad treatment of the subject object recognition in images and video. Showing interesting examples and references to the needed resources. Good explanation about which versions of different python packages should be used for successful results.
* Shihab (5 stars): It was a really amazing course. Must recommend for everyone.
* Estanislau de Sena Filho (5 stars): Excellent course. Excellent explanation. It's the best machine learning course for computer vision. I recommend it
* Areej AI Medinah (5 stars): The course is really good for computer vision. It consists of all material required to put computer vision projects in practice. After building a great understanding through theory, it also gives hands-on experience.
* Dave Roberto (5 stars): The course is completely worth it. The teacher clearly conveys the concepts and it is clear that he understands them very well (there is not the same feeling with other courses). The schemes he uses are not the usual ones you can see in other courses, but they really help much better to illustrate and understand. I would give eight stars to the course, but the maximum is five. It's one of the few Udemy courses that has left me really satisfied.
The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: email@example.com or by Twitter: @AILearningCQ