Python Deep Learning Solutions

About this course

Deep Learning is revolutionizing a wide range of industries. For many applications, Deep Learning has been proven to outperform humans by making faster and more accurate predictions. This course provides a top-down and bottom-up approach to demonstrating Deep Learning solutions to real-world problems in different areas. These applications include Computer Vision, Generative Adversarial Networks, and time series. This course presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, it provides a discussion on the corresponding pros and cons of implementing the proposed solution using a popular framework such as TensorFlow, PyTorch, and Keras. The course includes solutions that are related to the basic concepts of neural networks; all techniques, as well as classical network topologies, are covered. The main purpose of this video course is to provide Python programmers with a detailed list of solutions so they can apply Deep Learning to common and not-so-common scenarios.

About the AuthorIndra den Bakker is an experienced Deep Learning engineer and mentor. He is the founder of 23insights (part of NVIDIA's Inception program), a machine learning start-up building solutions that transform the world's most important industries. For Udacity, he mentors students pursuing a Nanodegree in Deep Learning and related fields, and he is also responsible for reviewing student projects. Indra has a background in computational intelligence and worked for several years as a data scientist for IPG Mediabrands and Screen6 before founding 23insights.

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