What you would learn in Computer Vision with Deep Learning course?
Stay current with the most cutting-edge technologies in Machine Learning and develop a professional portfolio by studying Computer Vision and Deep Learning fundamental notions, Object Detection, Image Classification, and Object Tracking.
The latest developments in Machine Learning technology have brought massive change, and most businesses are shifting to technologically-enabled business models that Deep Learning and Computer Vision fuel. To remain competitive in the market, it is vital to keep up-to-date and acquire expertise in the latest techniques.
The course was designed to help you understand the fundamental notions that underlie Computer Vision and Deep Learning, including neural networks, ANN, CNN along with activation functions. After introducing these fundamentals, the course will explain the structure of object detection and demonstrate how it's distinct from object tracking. Further, it explains the most widely-used object detection model that has developed over time. For the first time, we will begin by examining the structure design of the R-CNN Model and then proceed to the FAST RCN Model, which is an advanced version of the R-CNN. Then, we will introduce the concept behind Region Proposal Network (RPN) and use it to construct the FASTER R-CNN MODEL. Finally, we will end this tradition with the R-FCN Model. The course will dive into the more advanced models for object detection, beginning at Retinanet, SSD, and then going over the YOLO series, where we will discuss YOLO V3 and YOLO V3 Tiny, and YOLOV4 models.
Then, we proceed to the next step in image classification, where image classification models use the output of objects that are detected to identify the input data better. We'll start with the basic machine learning algorithms for image classification, such as the Support Vector Machines(SVM), Decision Tree, and K Nearest Neighbor(KNN), before moving on to more advanced algorithms such as VGG-16 ResNet50, Inceptionv3, and EfficientNet Model.
At the end of the course, the course will shift to the concept of Object Tracking, in which, after knowing the objects present in a video, we begin recording them in the video is processed them. Within the context of Object Tracking, we will discuss Meanshift Algorithm, SORT, and DeepSort Framework.
The course was designed to help explain a deep understanding of computer vision and machine learning in detail by first describing the basic concepts of technology and then applying code. An extensive walkthrough of code is included for all project implementations, and the source code is accessible to download. Furthermore, the course test will help you assess your understanding and determine areas to improve.
Join this course to get specialize in machine learning. Here are a few of the projects we'll be working on:
Use the pre-trained Faster R-CNN model to do object detection in a video
Develop Object Detection Application Automatic Number Plate Detection
Build and train a YOLOV3-based Object Detection model for License Number Plate Detection on cars.
Make use of the SVM model to label and classify the traffic signs in videos
Create and train ResNet Images Classification Model for identification of 20 classes of different types
The design football-playing Object tracking Application with SORT and YOLO
Learn Deep Learning basics - Neuron, Neural Network, and Activation Function
Learn the design and architecture for Object Detection Models like faster R-CNN RetinaNet, SDD, YOLO, and many more.
Develop Object Detection applications like Automatic License Number Plate etc
Learn the design and architecture for Image Classification Models such as SVM VGG-16 ResNet50 and InceptionV3.
Create Image Classification applications like Traffic Sign Board Detection etc.
Learn about the design of Object Tracking Frameworks such as Meanshift, SORT, and DeepSORT
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