GPU-Accelerated Deep Learning and AI Foundations

Deep learning is driving the modern AI revolution, but getting started with neural networks can feel overwhelming without a clear, structured path. This text-based course guides you through the foundational concepts of artificial intelligence and deep learning, preparing you to build and optimize your own models. You will transition from understanding basic AI terminology to confidently training neural networks, learning how to leverage GPU acceleration to speed up computation and apply transfer learning to solve real-world problems. In this course, you will: Learn the core principles of neural networks, activation functions, and backpropagation; Configure development environments for GPU-accelerated deep learning; Build and train image classification and natural language processing models from scratch; Apply transfer learning using pre-trained modern transformer and convolutional models; Optimize model performance using hyperparameter tuning and regularization techniques; Deploy trained models to production environments using basic MLOps workflows. The course begins with essential terminology and the mathematical foundations of AI before moving into practical code-based implementations. You will explore hands-on text-based exercises that demonstrate how to design, train, and evaluate deep learning models step-by-step. This course is designed for software developers, data analysts, and tech enthusiasts who are new to deep learning. No prior AI experience is required, though a basic understanding of Python programming is helpful. Start your journey into deep learning and build your first neural network today.