Machine Learning Lecture 10: Neural Networks and Deep Learning Feng Li fli@sdu.edu.cn https://funglee.github.io School of Computer Science and Technology Shandong University Fall 2018 Deep Feedforward f(x) is usually a highly non-linear function • Feedforward networks are of extreme importance to machine learning practioners • The conventional neural networks (CNN) used for object recognition from photos

Machine Learning Pytorch Tutorial TA : 曾元（Yuan Tseng） 2022.02.18 Outline ● Background: Prerequisites & What is Pytorch? ● Training & Testing Neural Networks in Pytorch ● Dataset & Dataloader ● Tensors year ■ ref: link1, link2 Some knowledge of NumPy will also be useful! What is PyTorch? ● An machine learning framework in Python. ● Two main features: ○ N-dimensional Tensor computation (like NumPy) translation, synthesis, ...) ○ Most implementations of recent deep learning papers ○ ... References ● Machine Learning 2021 Spring Pytorch Tutorial ● Official Pytorch Tutorials ● https://numpy.org/ Any questions

Lecture Notes on Support Vector Machine Feng Li fli@sdu.edu.cn Shandong University, China 1 Hyperplane and Margin In a n-dimensional space, a hyper plane is defined by ωT x + b = 0 (1) where ω ∈ Rn defined as γ = min i γ(i) (6) 1 ? ? ! ? ! Figure 1: Margin and hyperplane. 2 Support Vector Machine 2.1 Formulation The hyperplane actually serves as a decision boundary to differentiating positive we can construct a infinite number of hyperplanes, but which one is the best? Supported Vector Machine (SVM) answers the above question by maximizing γ (see Eq. (6)) as follows max γ,ω,b γ s.t. y(i)(ωT

Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) SVM December 28, 2021 1 / 82 Outline 1 SVM: A Primal Form 2 Convex Optimization Review (b < 0 means in opposite direction) Feng Li (SDU) SVM December 28, 2021 3 / 82 Support Vector Machine A hyperplane based linear classifier defined by ω and b Prediction rule: y = sign(ωTx + b) Given: " such that min& ' & !() & + " = 1 Feng Li (SDU) SVM December 28, 2021 14 / 82 Support Vector Machine (Primal Form) Maximizing 1/∥ω∥ is equivalent to minimizing ∥ω∥2 = ωTω min ω,b ωTω s.t. y(i)(ωTx(i)

addition to this, it will be very helpful, if the readers have a sound knowledge of Python and Machine Learning. Copyright & Disclaimer  Copyright 2019 by Tutorials Point (I) Pvt. Ltd. All Keras 1 Deep learning is one of the major subfield of machine learning framework. Machine learning is the study of design of algorithms, inspired from the model of human brain etc., for creating deep learning models. Overview of Keras Keras runs on top of open source machine libraries like TensorFlow, Theano or Cognitive Toolkit (CNTK). Theano is a python library used for

Notebook ��� TensorFlow “Hello TensorFlow” Try it ������ TensorFlow VM vs Docker Container Virtual Machine Docker Container � Docker ��� TensorFlow https://hub.docker.com/editions/community/docker-ce-desktop-mac

software that you installed to prepare to run NGC containers on TITAN PCs, Quadro PCs, or NVIDIA Virtual GPUs (vGPUs). Procedure 1. Issue the command for the applicable release of the container that is similar to the model that is discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. This model script is available on GitHub and is similar to the model that is discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper. PyTorch Release 23.06 PyTorch RN-08516-001_v23

1 / 57 Lecture 1: Overview 1 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 / 57 Course Information We will investigate fundamental concepts, techniques and algorithms in machine learning. The topics include linear regression, logistic re- gression, regularization, Gaussian discriminant Hang Li, Statistical Machine Learning (2nd Ed.), The Tsinghua Press, 2019 Zhihua Zhou, Machine Learning, Tsinghua Press, 2016 Tom M. Mitchell, Machine Learning (1st Ed.), China Machine Press, 2008 Ian Goodfellow

start off on our journey to more efficient deep learning models. Introduction to Deep Learning Machine learning is being used in countless applications today. It is a natural fit in domains where there problems where we expect exact optimal answers, machine learning applications can often tolerate approximate responses, since often there are no exact answers. Machine learning algorithms help build models, which Relation between Artificial Intelligence, Machine Learning, and Deep Learning. Deep learning is one possible way of solving machine learning problems. Machine learning in turn is one approach towards

examples / more than two features? In those cases, we could use classical machine learning algorithms like the Support Vector Machine4 (SVM) to learn classifiers that would do this for us. We could rely on embeddings for the inputs using machine learning algorithms of your choice. 2. Embedding Lookup: Look up the embeddings for the inputs in the embedding table. 4 Support Vector Machine - https://en.wikipedia. org/wiki/Support-vector_machine 3. Train the model: Train the model for the task at hand5 with the embeddings as input. Refer to Figure 4-4 that describes the three steps visually. Figure 4-4: A high-level