Course description
Computervision With TensorFlow
Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This course will help you explore 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 starts 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.
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Who should attend?
This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2
Pre-Requisites: Students should have:
- Basic to Intermediate IT Skills. have some knowledge of Python.
- Good basic understanding of image representation (pixels, channels, and so on)
- Understanding of Matrix manipulation (shapes, products, and so on)
Training content
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
- Technical requirements
- Getting started with TensorFlow 2 and Keras
- TensorFlow 2 and Keras in detail
- The TensorFlow ecosystem
Modern Neural Networks
- Technical requirements
- Discovering convolutional neural networks
- Refining the training process
Influential Classification Tools
- Technical requirements
- Understanding advanced CNN architectures
- Leveraging transfer learning
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
- Technical requirements
- Transforming images with encoders-decoders
- Understanding semantic segmentation
Training on Complex and Scarce Datasets
- Technical requirements
- Efficient data serving
- How to deal with data scarcity
Video and Recurrent Neural Networks
- Technical requirements
- Introducing RNNs
- Classifying videos
Optimizing Models and Deploying on Mobile Devices
- Technical requirements
- Optimizing computational and disk footprints
- On-device machine learning
- Example app – recognizing facial expressions
Costs
- Price: $2,895.00
- Discounted Price: $1,881.75
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Trivera Technologies
Trivera Technologies is a IT education services & courseware firm that offers a range of wide professional technical education services including: end to end IT training development and delivery, skills-based mentoring programs,new hire training and re-skilling services, courseware licensing and...