Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This Computer Vision with TensorFlow training course will explore TensorFlow 2, Google's open-source framework for machine learning. Join us to learn how to use convolutional neural networks (CNNs) for your visual tasks. This course starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network. You'll discover the features that 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, this course shares 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. In this course, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.
By attending Computer Vision with TensorFlow workshop, delegates will learn 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
- Basic Python skills
- Good basic understanding of image representation (pixels, channels, etc.)
- Understanding of Matrix manipulation (shapes, products, etc.)