Deep Learning Frameworks focuses on the tools and libraries used to design, train, and deploy neural network models efficiently. It enables developers and data scientists to build scalable AI applications for computer vision, natural language processing, and predictive analytics. This training explains popular platforms such as TensorFlow, PyTorch, Keras, and MXNet, along with their architectures and workflows. It also covers model building, automatic differentiation, GPU acceleration, distributed training, and deployment techniques. You will learn how organizations use these technologies to create high-performance AI solutions for real-world applications. The course also highlights best practices for selecting, optimizing, and managing neural network development environments in production systems.
