Over four weeks, Deep Learning Essentials takes you from foundational concepts to cutting‑edge applications.
- Week 1 demystifies what deep learning is—clarifying distinctions between AI, ML and DL—while unpacking why neural networks excel at modeling complex data.
- Week 2 dives into the mechanics of training: gradient descent, backpropagation, loss functions, learning rates and optimizers, giving you hands‑on practice tuning models for convergence and stability.
- In Week 3, you’ll apply these principles to computer vision, building convolutional neural networks that learn hierarchical feature representations and tackle real‑world image tasks.
- Week 4 brings it all together: you’ll explore state‑of‑the‑art MLPs, Transformers and CNN variants, leveraging pre‑trained architectures to solve advanced problems.
By the end of the course, you’ll gain an understanding of the concepts and methods of deep learning and how to utilize them to solve real-world problems. You’ll be able to apply deep learning to a variety of tasks and develop the skills to build deep learning models of your own.