Requirements

• Core Knowledge Solid understanding of Deep Learning Fundamentals: You should be comfortable with the core concepts of neural networks
• including backpropagation
• gradient descent
• and loss functions. You should know the difference between training
• validation
• and testing. Experience with Foundational Architectures: You need prior hands-on experience with building and training basic Convolutional Neural Networks (CNNs) for image classification and Recurrent Neural Networks (RNNs) or LSTMs for sequence data. Familiarity with University-Level Math: A solid foundation in Linear Algebra (vectors
• matrices
• dot products)
• Calculus (derivatives
• gradients)
• and Probability & Statistics (distributions
• mean
• variance) is essential. Programming Skills Proficient in Python: You must have strong Python programming skills
• including a solid grasp of data structures (lists
• dictionaries)
• classes
• and functions. Hands-on Experience with a Deep Learning Framework: You need to be proficient in at least one major deep learning library such as PyTorch (preferred for this course) or TensorFlow/Keras. You should be able to build models
• create data loaders
• and write a complete training loop from scratch. This course is designed for those who have moved past the introductory "what is a neural network?" stage and are ready to dive into the complex and powerful architectures that define the cutting edge of AI today.

Description

Who this course is for:

• If youre a student or professional aiming for a career in AI research
• machine learning engineering
• or a specialized role like Computer Vision or NLP Engineer
• this course is for you. It will give you the in-depth knowledge of the state-of-the-art architectures that are essential for top-tier roles and graduate-level research.