Welcome to Modern Computer Vision™ Tensorflow, Keras & PyTorch course – an indispensable guide to computer vision applications using the latest technologies and tools. This comprehensive course aims to teach students the foundations of computer vision, including classical computer vision (using OpenCV) and deep learning in Tensorflow, Keras, and PyTorch.
The Exciting Field of Computer Vision
The possibilities of computer vision are endless, transforming various industries from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing. Machine learning and artificial intelligence are revolutionizing the field of computer vision, allowing computers and robots to understand what they see through cameras or images.
Due to the exponential rise in demand for computer vision experts, now is the perfect time to learn, and this course aims to get beginners started. This course is tailored to provide learners with a comprehensive understanding of the foundations of computer vision and the opportunities available in this fast-paced industry.
The Instructor and Curriculum
The instructor has over 20 years of experience in content writing for top-ranking course review websites and knows what it takes to create quality content. The course curriculum is tailored to provide learners with 27+ hours of current and relevant computer vision theory, including practical example code to give students grounded working skills while they learn.
In addition, this course is taught using Google Colab Notebooks, which means there are no messy installations, and the code works straight away. This course is broken up into two main sections: classical computer vision (using OpenCV) and detailed deep learning
The Comprehensive Curriculum
The course curriculum is well-rounded, covering topics such as:
Classical Computer Vision:
- Image Operations and Manipulations
- Contours and Segmentation
- Facial Landmarks, Recognition and Face Swaps
- Video and Video Streams
- CNN Analysis and Advanced CNN Techniques
Deep Learning Syllabus:
- Classification with CNNs
- Transfer Learning and Fine Tuning
- Generative Adversarial Networks
- Autoencoders
- Modern CNN Architectures
- Siamese Networks for Image Similarity
- Facial Recognition (Age, Gender, Emotion, Ethnicity)
- Object Detection with YOLOv5 and v4, EfficientDetect, SSDs, Faster R-CNNs, and more
- Deep Segmentation - MaskCNN, U-NET, SegNET, and DeepLabV3
- Tracking with DeepSORT
- Deep Fake Generation
- Optical Character Recognition (OCR)
- 3D Computer Vision using Point Cloud Data
- Medical Imaging - X-Ray Analysis and CT-Scans
- Depth Estimation
- Making a Computer Vision API with Flask
Classical Computer Vision Projects:
The course also includes a variety of projects to get hands-on experience with classical computer vision, including sorting contours by size and location, image similarity, motion tracking, facial landmark detection, and more.
Deep Learning Computer Vision Projects:
For deep learning, this course includes PyTorch & Keras CNN Tutorial MNIST, ranking accuracy, transfer learning and fine-tuning, object detection, Siamese networks, facial recognition, deep segmentation, tracking, and more.
Course Ratings and Reviews
This course has 989 reviews and a rating aggregate of 4.45645 stars, making it a top-ranked course on the review websites. With such a high rating and in-depth coursework, it’s no surprise that many learners are finding the course incredibly beneficial to their career growth.
Why Choose This Course?
This course is perfect for those who are interested in learning computer vision and building a foundation for their career. The curriculum is well-rounded and taught by an experienced instructor, making it comprehensive and high-quality. With the use of Google Colab Notebooks, students will have no trouble following along with the course material and developing real-world skills set upon completion of the course.
The course material is up to date and relevant, covering a variety of topics and projects. Students will have hands-on experience with classical computer vision and deep learning, making them a desirable candidate for employers in the computer vision industry.
Get Started with Modern Computer Vision
Don't wait any longer to learn modern computer vision with this comprehensive course that covers classical computer vision and deep learning with PyTorch, Tensorflow2 Keras, and OpenCV4. With a high rating and excellent curriculum, this course promises to provide you with the necessary foundations and skills you need to thrive in the computer vision industry.
