Professional ML / AI
Machine learning was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. It is an application of artificial intelligence. It provides systems the ability to automatically learn and improve from experience with minimal human intervention and without being explicitly programmed. In addition to that, machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Starting with python basics, which are needed when using machine learning frameworks APIs and for doing labs in this course using Jupyter Notebooks, the principles and practices of supervised learning and deep learning, this course continues showing how to use neural networks to solve regression and classification problems and the main aspects of machine learning and artificial intelligence. It is also important to prepare your datasets for machine learning. This course also explains cloud- and edge-based applications and use cases. After an introduction to machine learning solutions for cloud and edge applications, this course shows how you can develop your own cloud- or edge-based applications using xilinx technology. It will be discribed, how to use XILINX machine learning solutions for data center and cloud-based applications and how to use DNN algorithms, models, inference and training, and frameworks on an edge computing platform.
The XILINX Versal, industry’s first ACAP (Adaptive Compute Acceleration Platform) offers breakthrough adaptive inference acceleration, advanced signal processing and a very high memory bandwidth. In addition to that, the ACAP offers a re-architected hardware fabric for high compute density per logic core, is capable of embedded real-time response and safety critical processing. Compared to CPUs and GPUs, the xilinx technology is more adaptive to machine learning algorithms. This course also offers practical hands-on labs for consolidating the knowledge, which is transfered in the different chapters in theory.
Basic knowledge of processor architectures
Basic knowledge of FPGA architectures