MLx Fundamentals

In this module, students will explore the foundational concepts of deep learning, including neural networks, CNNs, and RNNs. They will gain insights into advanced topics such as backpropagation, attention mechanisms, and the transformative role of architectures like BERT in natural language processing. This knowledge is crucial for applying deep learning techniques in real-world scenarios.

1. 1. Talk

In this module, students will explore essential classical machine learning algorithms, focusing on the bias-variance trade-off, supervised and unsupervised learning techniques, and methods to enhance model performance. Key concepts such as regularization, ensemble learning, and evaluation metrics will be covered, equipping students with the knowledge to build and assess predictive models effectively.

1. 1. Talk

In this module, students will delve into the core principles of machine learning systems, focusing on the integration of algorithms and system design. They will learn about the evolution of frameworks like TensorFlow and PyTorch, distributed training techniques, and the significance of computation graphs. By examining real-world applications, students will gain insights into the challenges and solutions in scaling machine learning models.

1. 1. Talk

In this module, students will delve into the principles and applications of Bayesian optimization, focusing on Gaussian processes, acquisition functions, and the integration of deep metric learning. They will learn to navigate the challenges of high-dimensional spaces and enhance optimization efficiency, gaining valuable insights applicable to fields like hyperparameter tuning and molecular design.

1. 1. Talk