Deep Learning with Vision Systems | Hands-On Computer Vision (TTAI3020)

Course Objectives

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 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

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be easily adjusted to accommodate.  We offer additional related deep learning, AI, data science, programming (Python, R, Java, Scala etc.) and development courses which may be blended with this course for a track that best suits your learning objectives. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2

Pre-Requisites:  Students should have

• Basic to Intermediate IT Skills. have some knowledge of Python.
• Good basic understanding of image representation (pixels, channels, etc.)
• Understanding of Matrix manipulation (shapes, products, etc.)

Take Before: We recommend attendees have the skills in the course(s) listed below, or attend them as a pre-requisite:

• TTPS4800 Introduction to Python Programming Basics

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We will work with you to tune this course and level of coverage to target the skills you need most.

1. 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

2. 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

3. Modern Neural Networks

• Modern Neural Networks
• Technical requirements
• Discovering convolutional neural networks
• Refining the training process

4. Influential Classification Tools

• Influential Classification Tools
• Technical requirements
• Understanding advanced CNN architectures
• Leveraging transfer learning

5. 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

6. Enhancing and Segmenting Images

• Enhancing and Segmenting Images
• Technical requirements
• Transforming images with encoders-decoders
• Understanding semantic segmentation

7. Training on Complex and Scarce Datasets

• Training on Complex and Scarce Datasets
• Technical requirements
• Efficient data serving
• How to deal with data scarcity

8. Video and Recurrent Neural Networks

• Video and Recurrent Neural Networks
• Technical requirements
• Introducing RNNs
• Classifying videos

9. 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

Course Materials

Student Materials: Each participant will receive a digital Student Guide and/or Course Notes, code samples, software tutorials, step-by-step written lab instructions (as applicable), diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment, or local installation, is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. In some cases we can also help you install this course locally if preferred. Please inquire for details and options.

Every-Course Extras = High-Value & Long-Term Learning Support! All Public Schedule courses include our unique EveryCourse Extras package (Course Recordings, Live Instructor Follow-on Support, Free *Live* Course Refresh Re-Takes, early access to Special Offers, Free Courses & more). Please inquire for details.