Advanced Computer Vision with TensorFlow

Colleagues, the Advanced Computer Vision with TensorFlow training program will enable you to explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection, apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images, implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more., and identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet. Gain high-demand skills in Salience, Image Segmentation, Model Interpretability, Class Activation Maps and TensorFlow Object Detection API. Training modules that will help advance your career include: 1) Introduction to Computer Vision - 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, 2) Object Detection - 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, 3) Image Segmentation - using variations of the fully convolutional neural network, 4) Visualization and Interpretability - learn about the importance of model interpretability, which is the understanding of how your model arrives at its decisions and  implement class activation maps, saliency maps, and gradient-weighted class activation maps.

Enroll today (individuals & teams welcome): https://tinyurl.com/drm2pss 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy