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.

1. 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.

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

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

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

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

6. 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.

7. At the final step, you'll pass Practice Test according to the all learned material during the course.

8. As a bonus part, you'll generate up to 1 million additional images and extend prepared dataset by new images via image rotation, image projection and brightness changing.

The main goal of the course is to develop and improve your hard skills in order to apply them for real problems of Image Classification based on Convolutional Neural Networks.

Every lecture of the course has SMART objectives. It means, that you can track your progress and witness practical results within the visible time frame, right after the end of the lecture.

• S - specific (the lecture has specific objectives)

• M - measurable (results are reasonable and can be quantified)

• A - attainable (the lecture has clear steps to achieve the objectives)

• R - result-oriented (results can be obtained by the end of the lecture)

• T - time-oriented (results can be obtained within the visible time frame)