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 2 is a hands-on course that thoroughly explores TensorFlow 2, 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. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have 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, the course demonstrates 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.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning 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 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

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 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


Audience

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

Language

English

Prerequisites

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

$2,495

Length: 3.0 days (24 hours)

Level:

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