This course thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. This course begins with the fundamentals of Computervision 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. 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.

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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
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

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.) TTPS4800 Introduction to Python Programming Basics

$2,495

Length: 4.0 days (32 hours)

Level:

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