Convolutional Neural Networks for Image Classification

In this practical course, you’ll design, train and test your own Convolutional Neural Network (CNN) for the tasks of Image Classification.

By the end of the course, you’ll be able to build your own applications for Image Classification.

At the beginning, you’ll implement convolution, pooling and combination of these two operations to grayscale images by the help of different filters, pure Numpy library and ‘for’ loops. We will also implement convolution in Real Time by camera to detect objects edges and to track objects movement.

After that, you’ll assemble images together, compose custom dataset for classification tasks and save created dataset into a binary file.

Next, you’ll convert existing dataset of Traffic Signs into needed format for classification tasks and save it into a binary file.

Then, you’ll apply preprocessing techniques before training, produce and save processed datasets into separate binary files.

At the next step, you’ll construct CNN models for classification tasks, select needed number of layers for accurate classification and adjust other parameters.

When the models are designed and datasets are ready, you’ll train constructed CNNs, test trained models on completely new images, classify images in Real Time by camera and visualize training process of filters from randomly initialized to finally trained.