Computer Vision with TensorFlow
Computer Vision with TensorFlow Course Details:
Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This 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. During class, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.
Call (919) 283-1653 to get a class scheduled online or in your area!
Computer Vision and Neural Networks
- Computer Vision and Neural Networks
- Technical requirements
- Computer vision in the wild
- A brief history of computer vision
- Getting started with neural networks
TensorFlow Basics and Training a Model
- TensorFlow Basics and Training a Model
- Technical requirements
- Getting started with TensorFlow 2 and Keras
- TensorFlow 2 and Keras in detail
- The TensorFlow ecosystem
Modern Neural Networks
- Modern Neural Networks
- Technical requirements
- Discovering convolutional neural networks
- Refining the training process
Influential Classification Tools
- Influential Classification Tools
- Technical requirements
- Understanding advanced CNN architectures
- Leveraging transfer learning
Object Detection Models
- Object Detection Models
- Technical requirements
- Introducing object detection
- A fast object detection algorithm – YOLO
- Faster R-CNN – a powerful object detection model
Enhancing and Segmenting Images
- Enhancing and Segmenting Images
- Technical requirements
- Transforming images with encoders-decoders
- Understanding semantic segmentation
Training on Complex and Scarce Datasets
- Training on Complex and Scarce Datasets
- Technical requirements
- Efficient data serving
- How to deal with data scarcity
Video and Recurrent Neural Networks
- Video and Recurrent Neural Networks
- Technical requirements
- Introducing RNNs
- Classifying videos
Optimizing Models and Deploying on Mobile Devices
- Optimizing Models and Deploying on Mobile Devices
- Technical requirements
- Optimizing computational and disk footprints
- On-device machine learning
- Example app – recognizing facial expressions
*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.
Join an engaging hands-on learning environment, where you’ll 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
This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.
To gain the most from this course, you should have:
- Basic to Intermediate IT skills
- Basic Python skills
- Good basic understanding of image representation (pixels, channels, etc.)
- Understanding of Matrix manipulation (shapes, products, etc.)
Data Scientist, Data Engineer, Software Engineer, and Developer