# 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 usually a highly non-linear function • Feedforward networks are of extreme importance to machine learning practitioners - The conventional neural networks (CNN) used for object recognition from photos h^{[2]} $ Output y ### Neural Feedforward Networks (Contd.) • We approximate $ f^{*}(x) $ by learning $ f(x) $ from the given training data • In the output layer, $ f(x) \approx y $ for each

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# Chapter 3 - Learning Techniques "The more that you read, the more things you will know. The more that you learn, the more places you'll go." — Dr. Seuss Model quality is an important benchmark benchmark to evaluate the performance of a deep learning model. A language translation application that uses a low quality model would struggle with consumer adoption because it wouldn't serve its flexibility to trade off some quality for smaller footprints. In the first chapter, we briefly introduced learning techniques such as regularization, dropout, data augmentation, and distillation to improve quality

## 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 link3  ### 2. Deep Learning Basics ■ Prof. Lee’s 1 $ ^{st} $ & 2 $ ^{nd} $ lecture videos from last year ref: link1 [Image](/uploads/documents/7/6/e/1/76e1a67e96719ae74c41b110fe07bfe6/p3_2.jpg) ## What is PyTorch? - An machine learning framework in Python. • Two main features: ○ N-dimensional Tensor computation (like NumPy) on GPUs

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# Solving Nim by the Use of Machine Learning Exploring How Well Nim Can be Played by a Computer Mikael Nielsen Røykenes  UNIVERSITY OF OSLO Autumn 2019 Powered by TCPDF (www.tcpdf.org) # Solving Nim by the Use of Machine Learning Exploring How Well Nim Can be Played by a Computer Mikael Nielsen Røykenes  © 2019 Mikael Nielsen Røykenes Solving Nim by the Use of Machine Learning http://www.duo.uio.no/ Printed: Reprosentralen, University of Oslo ## Contents 1 Intro 3 1

Advanced Learning Techniques “Tell me and I forget, teach me and I may remember, involve me and I learn.” – Benjamin Franklin This chapter is a continuation of Chapter 3, where we introduced learning techniques techniques. To recap, learning techniques can help us meet our model quality goals. Techniques like distillation and data augmentation improve the model quality, without increasing the footprint of the model this chapter by presenting self-supervised learning which has been instrumental in the success of natural language models like BERT. Self-Supervised learning helps models to quickly achieve impressive

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