Advanced Computer Vision with TensorFlow

Go to Course: https://www.coursera.org/learn/advanced-computer-vision-with-tensorflow

Introduction

### Course Review: Advanced Computer Vision with TensorFlow on Coursera **Overview:** If you are keen on diving into the world of computer vision, the course "Advanced Computer Vision with TensorFlow" on Coursera offers a comprehensive curriculum designed to equip learners with the necessary skills to tackle sophisticated image processing tasks. This course thoughtfully balances theoretical knowledge with hands-on practice, ensuring that you not only understand the concepts but also effectively apply them. **Course Structure and Key Topics:** 1. **Introduction to Computer Vision:** The course kicks off with a solid foundation, covering essential concepts such as image classification, object localization, object detection, and image segmentation. You will also learn to differentiate between multi-label classification, semantic segmentation, and instance segmentation, which are crucial for advanced applications in computer vision. 2. **Object Detection:** This section is particularly exciting as it delves into popular object detection models like regional-CNN and ResNet-50. The course emphasizes practical learning; not only will you utilize detection models from TensorFlow Hub, but you will also customize and create your own models. A standout feature of this module is the task of training a model to detect and localize rubber duck images using just five training examples, making the learning process engaging and relatable. 3. **Image Segmentation:** This week focuses extensively on the nuances of image segmentation, utilizing various architectures of the fully convolutional network (FCN). You will learn to assign pixel-wise class labels, enabling detailed identification of objects. The practical applications include building U-Net and Mask R-CNN to identify diverse categories such as numbers, pets, and even zombie figures. This hands-on approach solidifies your understanding of more complex identification techniques. 4. **Visualization and Interpretability:** The final module highlights the fate of model interpretability, a key aspect in the deployment of AI and machine learning applications. You will explore techniques such as class activation maps and saliency maps to understand how your models arrive at their predictions, fostering a deeper comprehension of model workings. It's fascinating to see how visualizing activations can optimize existing neural networks, specifically the renowned AlexNet. **Learning Experience:** The course is well-structured, making it accessible both for learners with foundational knowledge in machine learning and for those looking to scale their expertise in computer vision. The engaging content, bolstered by a plethora of coding exercises and practical projects, ensures that learners can apply the skills in real-world scenarios—an invaluable aspect of any educational endeavor. **Recommended For:** I highly recommend this course for data scientists, ML practitioners, and enthusiasts eager to enhance their abilities in computer vision. It serves as an excellent stepping stone towards more advanced studies in AI and computer vision fields. Whether you are looking to build robust solutions for image-related challenges or simply wish to bolster your portfolio with cutting-edge skills, this course is a worthy investment of your time. ### Conclusion: In summary, "Advanced Computer Vision with TensorFlow" on Coursera is a well-crafted course that offers a deep dive into the intricate world of computer vision while maintaining a strong practical orientation. Whether you're looking to upgrade your skills or embark on a new project, this course equips you with the right tools and knowledge to succeed. Dive in and start building your models today!

Syllabus

Introduction to Computer Vision

Get a conceptual overview of image classification, object localization, object detection, and image segmentation. Also be able to describe multi-label classification, and distinguish between semantic segmentation and instance segmentation. In the rest of this course, you will apply TensorFlow to build object detection and image segmentation models.

This week, you’ll get an overview of some popular object detection models, such as regional-CNN and ResNet-50. You’ll use object detection models that you’ll retrieve from TensorFlow Hub, download your own models and configure them for training, and also build your own models for object detection. By using transfer learning, you will train a model to detect and localize rubber duckies using just five training examples. You’ll also get to manually label your own rubber ducky images!

This week is all about image segmentation using variations of the fully convolutional neural network. With these networks, you can assign class labels to each pixel, and perform much more detailed identification of objects compared to bounding boxes. You’ll build the fully convolutional neural network, U-Net, and Mask R-CNN this week to identify and detect numbers, pets, and even zombies!

This week, you’ll learn about the importance of model interpretability, which is the understanding of how your model arrives at its decisions. You’ll also implement class activation maps, saliency maps, and gradient-weighted class activation maps to identify which parts of an image are being used by your model to make its predictions. You’ll also see an example of how visualizing a model’s intermediate layer activations can help to improve the design of a famous network, AlexNet.