Institution: Coursera

Course 1 of the Deep Learning Specialization. Covers the foundations of deep learning, including neural network architecture, forward and backward propagation, and building deep neural networks.

Course 5 of the Deep Learning Specialization. Covers RNNs, GRUs, LSTMs, attention mechanisms, and transformer networks.

Course 4 of the Deep Learning Specialization. Covers CNN architectures, object detection, face recognition, and neural style transfer.

Course 3 of the Deep Learning Specialization. Covers ML strategy, error analysis, and techniques for building successful machine learning projects.

Course 2 of the Deep Learning Specialization. Covers optimization algorithms, hyperparameter tuning, batch normalization, and programming frameworks.

The Deep Learning Specialization is a foundational program that teaches the capabilities, challenges, and consequences of deep learning and prepares learners to participate in the development of leading-edge AI technology.

This specialization consists of five courses covering neural networks, deep learning optimization, structuring ML projects, convolutional neural networks, and sequence models.