These notes cover several aspects of machine learning and deep learning, such as:

Neural Networks & Deep Learning:

Introduction to neural networks and deep learning. Supervised learning, shallow, and deep neural networks. Associated Jupyter notebooks and resources for each week of study. Improving Deep Learning Networks:

Practical aspects like optimization algorithms, hyperparameter tuning, batch normalization, and more. Accompanying notebooks for each week's content. Structuring ML Projects:

Strategies for structuring machine learning projects, error analysis, and more. Relevant notebooks to guide project structuring. Convolutional Neural Networks (CNNs):

Foundations and architecture of CNNs, deep convolutional models, object detection, and special applications like face recognition and neural style transfer. Links to influential papers and practical notebooks. Sequence Models:

Although the sequence model details seem to be incomplete in your text, these usually cover Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and applications in sequence prediction.