Course Overview
TOPComputer Vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computer Vision with Tensorflow is a hands-on course that thoroughly explores TensorFlow, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.
Scheduled Classes
TOP| 12/11/23 - TTV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOPThis “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
Outline
TOP- 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
Prerequisites
TOPStudents 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.)
We recommend attendees have the skills in the course(s) listed below, or attend them as a pre-requisite:
- Introduction to Python Programming Basics
Who Should Attend
TOPThis course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2
