This course thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. This course begins with the fundamentals of Computervision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos.
* Actual course outline may vary depending on offering center. Contact your sales representative for more information.
This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to
Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras
Apply modern solutions to a wide range of applications such as object detection and video analysis
Run your models on mobile devices and web pages and improve their performance.
Create your own neural networks from scratch
Classify images with modern architectures including Inception and ResNet
Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net
Tackle problems faced when developing self-driving cars and facial emotion recognition systems
Boost your application’s performance with transfer learning, GANs, and domain adaptation
Use recurrent neural networks (RNNs) for video analysis
Optimize and deploy your networks on mobile devices and in the browser