Dropout留学生作业代做、代写Python课程设计作业、Python编程作业调试、代做Neural Networks作业代写留学生Prolog|帮做C/C

Assignment #2Fully-Connected Nets, Batch Normalization, Dropout, Convolutional NetsIn this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks. The goals of this assignment are as follows:understand Neural Networks and how they are arranged in layered architecturesunderstand and be able to implement (vectorized) backpropagationimplement various update rules used to optimize Neural Networksimplement batch normalization for training deep networksimplement dropout to regularize networkseffectively cross-validate and find the best hyperparameters for Neural Network architectureunderstand the architecture of Convolutional Neural Networks?and gain experience with training these models on dataGet the code as a zip file. [Updated: 5 Oct 2017]Important note from YY: we have updated the skeleton codes, please download it again if you downloaded it before 5 Oct 2017.Download data: Once you have the starter code, you will need to download the CIFAR-10 dataset. Run the following from the?assignment2 directory:cd cs231n/datasets?folder and click get_datasets.py to run the Python code to download the data.Submitting your work:Whether you work on the assignment locally or in the lab, once you are done working run the Python code?collectSubmission.py; this will produce a file calledassignment2.7z. Q1: Fully-connected Neural Network (25 points)The IPython notebook?FullyConnectedNets.ipynb will introduce you to our modular layer design, and then use those layers to implement fully-connected networks of arbitrary depth. To optimize these models you will implement several popular update rules.Q2: Batch Normalization (25 points)In the IPython notebook BatchNormalization.ipynb you will implement batch normalization, and use it to train deep fully-connected networks.Q3: Dropout (10 points)The IPython notebook Dropout.ipynb will help you implement Dropout and explore its effects on model generalization.Q4: Convolutional Networks (30 points)In the IPython Notebook ConvolutionalNetworks.ipynb you will implement several new layers that are commonly used in convolutional networks.Q5: PyTorch / TensorFlow on CIFAR-10 (10 points)For this last part, you will be working in either TensorFlow or PyTorch, two popular and powerful deep learning frameworks.You only need to complete ONE of these two notebooks.You do NOT need to do both, but a very small amount of extra credit will be awarded to those who do.Note: please complete only the Tensorflow notebook for our school version of the assignmenet because our WinPython package only supports Tensorflow.Open up either PyTorch.ipynb or TensorFlow.ipynb. There, you will learn how the framework works, culminating in training a convolutional network of your own design on CIFAR-10 to get the best performance you can.Q5: Do something extra! (up to +10 points)In the process of training your network, you should feel free to implement anything that you want to get better performance. You can modify the solver, implement additional layers, use different types of regularization, use an ensemble of models, or anything else that comes to mind. If you implement these or other ideas not covered in the assignment then you will be awarded some bonus points.本团队核心人员组成主要包括硅谷工程师、BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：[email protected] 微信：codehelp