Learn about Computer Vision, become knowledgeable of Computer Vision, and the process behind classic algorithmic solutions. Gain information on building and training deep neural networks.

Recurrent Neural Network

This module will provide the students with a brief introduction to RNN, its network structure, types, Bidirectional RNN, and its limitations. Additionally, it will deal with training an RNN with a use case and introduce the students to LSTM and its architecture and variants. The Recurrent Neural Network model will also help in time series forecasting or sequential modeling using LSTM.

Convolutional Neural Network

Introduction to CNN and Convolutional operations are what this module will deal with alongside its features, ReLu, Pooling, and fully connected layer. The students will learn about training a CNN and Image Classification.

Open CV

This module will give the students exposure to the introduction to CV and how to use OpenCV to work with image files. Additionally, the students will learn more about image manipulation involving smoothing and blurring, translation, rotation, and cropping. Learn about thresholding and morphological operations and how to open and stream video with OpenCV. Gain valuable information such as creating color histograms with OpenCV, face recognition, and template matching.

Advanced Computer Vision

Learn about transfer learning using Keras and gain information related to VGG, RESNET, object detection, drawing bounding boxes, and Yolo.

Deployment

This topic will discuss creating pickle and frozen files, cloud deploying machine learning, and Deep Learning models for production.