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Course Overview

Courses Overview

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About Course

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

COURSE SYLLABUS

  • Learn where computer vision techniques are used in industry

  • Prepare for the course ahead with a detailed topic overview

  • Start programming your own applications

     

  • See how images are represented numerically
  • Implement Image Processing techniques like colour and
  • Geometric Transforms
  • Learn about the layers of a Deep Convolutional Neural Network
  • Convolutional, Max Pooling, and Fully Connected Layers
  • Build a CNN-based Image Classifier in PyTorch
  • Learn about Layer Activation and Feature Visualization techniques

 

  • Learn why distinguishing features are important in pattern and object recognition tasks
  • Write code to extract information about an object’s colour and shape
  • Use features to identify areas on a face and to recognize the shape of a car or pedestrian on a road
  • Implement K-Means Clustering to break an image up into parts
  • Find the contours and edges of multiple objects in an image
  • Learn about background subtraction for video

 

Combine CNN and RNN knowledge to build a deep learning model that produces captions given an input
image. Image captioning requires that you create a complex deep learning model with two components: a CNN that transforms an input image into a set of features, and an RNN that turns those features into rich, descriptive language. In this project, you will implement these cutting-edge deep learning architectures.

Learn about advance CNN architectures

  • See how region-based CNN’s, like Faster R-CNN, have allowed for fast, localized object recognition in images
  • Work with a YOLO/single shot object detection system
  • Learn how Recurrent Neural Networks learn from ordered sequences of data
  • Implement an RNN for sequential text generation
  • Explore how memory can be incorporated into a Deep Learning model
  • Understand where RNN’s are used in deep learning applications
  • Learn how attention allows models to focus on a specific piece of input data
  • Understand where attention is useful in Natural Language and Computer Vision applications

 

  • Learn how to combine CNNs and RNNs to build a complex captioning model
  • Implement an LSTM for caption generation
  •  Train a model to predict captions and understand a visual scene

Use feature detection and key point descriptors to build a map of the environment with SLAM (Simultaneous Localization and Mapping). Implement a robust method for tracking an object over time, using elements of Probability, Motion Models, and Linear Algebra. This project tests your knowledge of localization techniques that are widely used in autonomous vehicle navigation

  • Learn how to programmatically track a single point over time
  • Understand motion models that define object movement over time
  • Learn how to analyse videos as sequences of individual image frames
  • Implement a method for tracking a set of unique features over time
  • Learn how to match features from one image frame to another
  • Track a moving car using optical flow

Course Content

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