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Computervision with TensorFlow

SS Course: 2001635

Course Overview


Computer 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

12/11/23 - TTV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn


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


Viewing outline for:
  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
  1. 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
  1. Modern Neural Networks
  • Modern Neural Networks
  • Technical requirements
  • Discovering convolutional neural networks
  • Refining the training process
  1. Influential Classification Tools
  • Influential Classification Tools
  • Technical requirements
  • Understanding advanced CNN architectures
  • Leveraging transfer learning
  1. 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
  1. Enhancing and Segmenting Images
  • Enhancing and Segmenting Images
  • Technical requirements
  • Transforming images with encoders-decoders
  • Understanding semantic segmentation
  1. Training on Complex and Scarce Datasets
  • Training on Complex and Scarce Datasets
  • Technical requirements
  • Efficient data serving
  • How to deal with data scarcity
  1. Video and Recurrent Neural Networks
  • Video and Recurrent Neural Networks
  • Technical requirements
  • Introducing RNNs
  • Classifying videos
  1. 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



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.)

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


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

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