Duration
4 Days
24 CPD hours
This course is intended for
This course is geared for attendees with Intermediate IT skills who wish to
learn Computer Vision with tensor flow 2
Overview
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
Computer vision solutions are becoming increasingly common, making their way
into fields such as health, automobile, social media, and robotics. Hands-On
Computervision 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. 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. In the process, you will acquire
advanced insights into transfer learning, data augmentation, domain adaptation,
and mobile and web deployment, among other key concepts.
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