The Applied Computer Vision training course provides the fundamentals of Deep Learning concepts. Currently, Deep Learning is the most exciting field of Machine Learning (ML). Deep Learning algorithms are giving state of the art results in almost every domain like computer vision, natural language processing, speech analysis, robotics, etc.
The course begins by teaching students the fundamentals of deep learning concepts through an advanced level of knowledge. Next, students are introduced to Neural Networks and ML for classification. The course concludes with a look at Image Captioning with Recurrent Neural Networks (RNNs) and preparing data for Mask RCNN.
After completing this course students will be able to design the Deep Neural Network architecture for various applications.
Purpose
|
Learn to understand, design, implement and assess the impact of deep learning techniques for a diverse range of visual recognition tasks. |
Audience
|
Developers who need to learn to work algorithms for visual recognition in a Machine Learning context. |
Role
| Data Engineer - Data Scientist - Software Developer |
Skill Level
| Intermediate |
Style
| Hack-a-thon - Learning Spikes - Workshops |
Duration
| 3 Days |
Related Technologies
| Machine Learning Training | Deep Learning |
Productivity Objectives
- Identify foundational concepts for representation learning using neural networks.
- Describe state-of-the-art models for tasks such as image classification, object detection, image segmentation, etc.
- Obtain practical experience in the implementation of visual recognition models using deep learning.
