Foundations of Neural Networks for CV and NLP

Are you fascinated by AI's ability to understand images and text but don't know where to start? This course provides a clear, text-based pathway into the world of neural networks. By the end of this course, you will possess a solid theoretical and practical understanding of how neural networks function and how they are applied to solve real-world problems in computer vision and natural language processing, equipping you with fundamental AI development skills. What you'll learn: * Learn the foundational concepts of artificial neurons, activation functions, and network architectures. * Understand how to train neural networks using backpropagation, gradient descent, and regularization techniques. * Apply modern convolutional neural network (CNN) architectures for image classification, object detection, and other computer vision tasks. * Explore recurrent neural networks (RNNs) and transformer models for sequential data, including text generation and sentiment analysis. * Practice implementing neural network components and training loops through written exercises and code snippets. * Analyze common challenges in neural network training, such as overfitting and vanishing gradients, and learn strategies to mitigate them. This course guides you from the very basics of neural network theory to practical applications in computer vision and natural language processing. You will progress through foundational concepts, explore key architectures, and then delve into how these models process visual and textual data. This course is designed for absolute beginners with no prior experience in neural networks or advanced machine learning. No prerequisites are required beyond basic programming literacy. Start your journey into the exciting field of AI and unlock the power of neural networks today.