these limited number of labeled examples for fine-tuning since the model already knows the general concepts about language, and use the same model across many tasks. Model reuse by itself also is a powerful in your experiments. Yet another way of improving generalization is to allow the model to learn concepts in the order of their difficulty. Curriculum learning shows us how. Curriculum Learning We know basic and easier to grasp fundamentals are taught first, followed by incrementally more difficult concepts that build upon previous lessons. The intuition behind this is the theory of Continuation Methods

About the Course 2 Machine Learning: What and Why? 3 Categories of Machine Learning 4 Some Basic Concepts of Machine Learning Feng Li (SDU) Overview September 6, 2023 2 / 57 Instructor Prof. Feng Li Li (SDU) Overview September 6, 2023 3 / 57 Course Information We will investigate fundamental concepts, techniques and algorithms in machine learning. The topics include linear regression, logistic re-

you comfortable in getting started with the Keras framework concepts. Prerequisites Before proceeding with the various types of concepts given in this tutorial, we assume that the readers have basic creating a model. Once the compilation is done, we can move on to training phase. Let us learn few concepts required to better understand the compilation process. Loss In machine learning, Loss function

graphs with different number of LSTM cells based on the sentence’s length. PyTorch • Fundamental Concepts of PyTorch • Tensors • Autograd • Modular structure • Models / Layers • Datasets • Dataloader •

floating point vector. These embedding tables are very useful, because they help us convert abstract concepts hidden in natural language into a mathematical representation that our models can use. The quality

It must fulfill the following goals: a) To compress the information content of high-dimensional concepts such as text, image, audio, video, etc. to a low-dimensional representation such as a fixed length