# 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 is 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

## 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 ### 1. Python3 ☑ if-else, loop, function, file I0, class, ... refs: link1, link2, link3  ### 2. Deep Learning Basics nts/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 Automatic

## Yandex ## Yandex # Machine Learning with ClickHouse ## Experimental dataset ## NYC Taxi and Uber Trips > Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page > sample of data is enough to start All you need is to get it from ClickHouse Couple of lines for Python + Pandas import requests import io import pandas as pd url = 'http://127.0.0.1:8123?query=' ClickHouse stochasticLinearRegression(parameters)(target, x₁, ..., xₙ) ## Available parameters: > learning_rate > I2_regularization > batch_size > optimizer: Adam, SGD, Momentum, Nesterov All

## Yandex ## Yandex # Machine Learning with ClickHouse ## Experimental dataset ## NYC Taxi and Uber Trips > Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page > sample of data is enough to start All you need is to get it from ClickHouse Couple of lines for Python + Pandas import requests import io import pandas as pd url = 'http://127.0.0.1:8123?query=' ClickHouse stochasticLinearRegression(parameters)(target, x₁, ..., xₙ) ## Available parameters: > learning_rate > I2_regularization > batch_size > optimizer: Adam, SGD, Momentum, Nesterov All

# 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

2.jpg) ## Python in Azure Functions ## 基于Python的Azure Functions实践 赵健 - Microsoft 目录 CONTENTS >> >> Python 在 Azure 中无处不在 >> 粘合剂 – Azure Functions >> Azure Functions 实践 ments/a/5/8/6/a58624fb90d74755f6f59626fe84b055/p3_2.jpg) ## Python 在 Azure 中无处不在 |Rank|Language|Type|Score| |---|---|---|---| |1|Python|☑|100.0| ||||| |2|Java|☑|96.3| ||||| |3|C|☑|94.4| ||||| |4|C++|☑|87 sixth annual interactive ranking of the top programming languages Top Analytics, Data Science, Machine Learning Software 2017-2019, KDnuggets Poll ![Image](/uploads/documents/a/5/8/6/a58624fb90d74755f6f59626fe84b055/p4_2

Conquer Machine Learning Lifecycle on Kubernetes  ## Brian Redmond • Cloud Architect @ Microsoft (18 years) • Azure Global ## Rita Zhang • Software engineer @ Microsoft, San Francisco • Azure Cloud Native Compute team • Kubernetes upstream features, Azure Kubernetes Service ![Image](/uploads/documents/6/6/a/3/66a388a3 Intro to Kubeflow, Helm, Argo ## • Demos • Image classification with Inception v3 and transfer learning • Automate repeatable ML experiments with containers • Deploy ML components to Kubernetes with

## Leveraging the Power of C++ for Efficient Machine Learning on Embedded Devices ADRIAN STANCIU # Leveraging the power of C++ for efficient machine learning on embedded devices Adrian Stanciu adrian Motivation ▶ Image classification ▶ Hand gesture recognition Summary ▶ Q&A Motivation ## Machine Learning (ML) ▶ Subfield of Artificial Intelligence (AI) Enables computers to learn from data and then consumption ▶ May have real-time performance constraints ## Machine learning on embedded devices Alternative to cloud-based machine learning Advantages: ▶ Real-time processing ▶ Low latency ▶ Reduced

C++ Machine Learning Library Gautam Sharma Independent Researcher gautamsharma2813@gmail.com Abstract—micrograd++ is a pure C++ machine learning library inspired by Andrej Karpathy's Python implementation framework for building and training machine learning models. By leveraging the performance efficiency of C++, micrograd++ offers a robust solution for integrating machine learning capabilities directly into C++-based Traditionally, all machine learning libraries are extremely bulky and very hard to integrate as third party dependencies. This aspect scares practitioners to adopt a C++ based machine learning library for prototyping

## ServiceComb java SDK详解 ## AGENDA 设计演进及架构 • 服务发现 • 微服务调用 • Edge Service • Metrics • 性能调优 ## 设计演进-初始 Consumer Transport Producer 透明RPC 治理 RPC 治理 透明RPC JAX-RS RestTemplate 治理 Servlet