What is Deep Learning

"What is Deep Learning" Deep Learning is a subset of machine learning that involves building and training artificial neural networks to make predictions and decisions based on large sets of data. It is a type of artificial intelligence that is used to recognize patterns in complex data and make predictions based on those patterns. This course "What is Deep Learning" is designed to provide a comprehensive introduction to deep learning, covering the fundamentals of neural networks, the mathematics behind them, and the latest developments in the field. The course begins by exploring the history and evolution of deep learning, starting with its roots in artificial intelligence and moving through its development in the 1990s and early 2000s, to its current state as one of the most exciting and rapidly evolving fields in machine learning. Participants will learn the basics of how deep learning works, including how artificial neural networks are constructed and trained to recognize patterns in data. They will learn about different types of neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and how they are used in image and speech recognition, natural language processing, and other applications. The course will also cover the latest techniques and tools, including TensorFlow and PyTorch, which are popular libraries used for building and training neural networks. Participants will gain hands-on experience building and training neural networks using these tools, and will learn best practices for optimizing their performance. Throughout the course, participants will be exposed to real-world examples of applications, such as self-driving cars, facial recognition, and speech recognition, and will learn about the ethical considerations involved in developing and deploying these technologies. By the end of the course, participants will have a solid understanding, how it works, and how it is used in a variety of applications. They will also have gained hands-on experience building and training neural networks, and will be well-prepared to explore more advanced topics. Author: (Udacity)