Deep Learning Fundamentals with Python

Course Description This comprehensive course provides a strong foundation in deep learning concepts and their Python implementation. Students will explore the core principles of neural networks, delve into key architectures like the Multilayer Perceptron (MLP), and learn the backpropagation algorithm. The course introduces powerful deep learning frameworks, TensorFlow and Keras, enabling you to build and train neural networks. Real-world applications, including stock price prediction and natural language processing, will be covered, along with model deployment using Flask or Streamlit. Prerequisites: > Basic understanding of Python programming. > Familiarity with basic machine learning concepts. > No prior deep learning experience required. Learning Outcomes: > Understand and explain fundamental concepts of neural networks and deep learning. > Build and train neural networks using TensorFlow and Keras. > Apply deep learning techniques to solve real-world problems. > Deploy deep learning models using Flask or Streamlit.