Description
During the course, students will get deep knowledge about Deep Learning. The course covers basic essentials about Deep Learning gradually covering more complicated topics. Practical application, use cases and problems that can be solved with Deep Learning will be discussed in the course.
Students will learn how to build successful projects in Deep Learning, what data requirements and metrics are needed to get the best results. They would learn how to set up a development cycle of projects and models improvement pipeline. After the course, students would understand what is convolution and the way the convolutional neural network works as well as how to build convolutional neural networks and apply it to image data.
Also, they would know the difference between balanced and unbalanced datasets, overfitting and underfitting problems, the way how to determine such a problem and effective ways for solving it.
Learning outcomes:
Students would get fundamental knowledge in Deep Learning basics and all necessary building blocks for advancing their level of proficiency in the future. Moreover, they would learn how to build and ship deep learning products as well as how to detect potential problems and potential way to solve them
Language: English
