Build a strong foundation in Neural Networks and Deep Learning, the core technologies behind modern Artificial Intelligence systems such as image recognition, speech processing, and autonomous systems.
This course is designed to help you understand both the theoretical concepts and practical implementation of deep learning models using industry-standard tools.
You will begin with an introduction to neural networks, understanding how artificial neurons work and how deep learning models are structured.
Next, you will learn forward propagation and activation functions, which define how data flows through a neural network and how decisions are made.
The course then dives into loss functions and backpropagation, the key mechanism that enables neural networks to learn from data and improve accuracy.
You will also explore gradient descent and optimization techniques, essential for training deep learning models efficiently.
On the practical side, you will build neural networks using both:
Finally, you will apply your knowledge in a hands-on deep learning project, building an image classification model using the CIFAR-10 dataset, followed by model evaluation and performance improvement.
Model Tuning and Optimization for Machine Learning
