Practical “Deep Learning”
What is Deep Learning?
- Deep learning is a subset of Artificial Intelligence (AI) that uses artificial neural networks, inspired by the way the human brain processes and learns information.
- It is a form of machine learning that learns complex patterns from large datasets to make predictions. Deep learning excels in many application areas, such as image recognition, speech recognition, and language translation.
Course Overview
- This course begins with foundational concepts of deep learning and advances into the latest models and techniques.
- You will apply deep learning to real-world problems using Python and key libraries, gaining practical capabilities through a combination of theory and hands-on practice.
- Topics covered include:
- Classification, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), reinforcement learning algorithms, model optimization, and more
Target Audience
- This course is intended for students who have basic skills in Python and data science.
- If you do not have this background, we recommend taking “Introduction to Data Science with Python” first.
Requirements
- Students: Personal laptop
- Institution: Classroom equipped with internet, HDMI, and projector
- AI Castle provides and prepares all other necessary resources (account setups, power strips, etc.).
Example Exercises
- Deep Neural Networks (DNN): Principles and Implementation
- Image Processing with CNNs (Convolutional Neural Networks)
- Time Series Processing with RNN, Attention, and Transformers
- Generative AI with GANs
Curriculum
- This course is optimized for 100 hours of instruction.
- A minimum of 60 hours and up to 120 hours is recommended.