. I need 2400 words on MNIST

Instructions 

Written Character Classification (MNIST) with via Multi-Layer Perception 

2400 words 

8 pages double space for report 

All files should be here in Google drive, 

https://drive.google.com/drive/folders/1i8565J1fBVSy-oBZQH7CHjsgHcEt95Jg 

Important note: 

One of the folders is called 'For week 8 MATLAB Demo ...in Google drive' that one make first (Look, Parent Session Due date) on the Pdf (initialize mlp for XOR). Rest make later (look Child session due date) 

The requirement file is attached

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Attachment 1;

MNIST Written CharacterClassification with a Multi-Layer Perceptron Learning Outcomes

1.Evaluate and articulate the issues and challenges in machine learning, including model selection, complexity and feature selection

2.Demonstrate a working knowledge of the variety of mathematical techniques normally adopted for machine learning problems, and of their application to creating effective solutions

3.Critically evaluate the performance and drawbacks of a proposed solution to a machine learning problem4.Create solutions to machine learning problems using appropriate software.

Data set

The dataset MNIST contains images of hand-written characters 0-9. The task is to take an image as input and determine which of the numbers 0-9 is written in it. The dataset is an industry standard and one of the most common benchmarks for new classification algorithms. The dataset and information about it can be found here: http://yann.lecun.com/exdb/mnist/ .

 
The dataset is already pre-structured into training and test sets, which can be downloaded on the homepage. Performances of common algorithms on this database are well known and also documented as error-rates on the test set on the homepage.The dataset can be easily read into Matlab or Octave with commonly available helper scripts such as on http://ufldl.stanford.edu/wiki/index.php/Using_the_MNIST_Dataset .   

% Change the filenames if you've saved the files under different names

% On some platforms, the files might be saved as % train-images.idx3-ubyte / train-labels.idx1-ubyte images = loadMNISTImages('train-images-idx3-ubyte'); labels = loadMNISTLabels('train-labels-idx1-ubyte');

% We are using display_network from the autoencoder codedisplay_network(images(:,1:100)); % Show the first 100 imagesdisp(labels(1:10));

  Machine Learning and Evaluation  

For this coursework you will program and use the Backpropagation learning algorithm for Multi-Layer Perceptrons (a.k.a. “Deep Learning”) in Matlab/Octave. The attached Matlab file provides a stub for the neural network code with data members and a constructor already in place. Fill in the methods to initialize the weights, to compute the output of the network for a given input, as well as methods to train the network by means of the backpropagation algorithm. Use of the template is compulsory. Implementations outside the template design will not be accepted unless explicitly and individually agreed by the module leader. The network implementation must have at least one hidden layer (as is provided). Backpropagation may be implemented as online algorithm.

Experiments must at least show:

•The training and test error

•A comparison of different hidden layer sizes

The entire experiment must be submitted as Matlab script file from which it can be reproduced. Indicate whether you developed in Matlab or Octave.

Bonus points are given for the implementation of more network layers, an additional batch-gradient implementation of backpropagation, or meaningful pre-processing steps.Further bonus points are given for use of a separate validation set, for crossvalidation, or experimental evaluation of any other relevant parameters.

Report structure and assessment (70% of module mark) 1)Write a brief introduction that introduces (10%)

1)Explains what MNIST is about and what its contents are, what relevant size characteristics are

2)Explain why MNIST is more challenging than the Iris dataset.

3)Briefly discuss key algorithm performances using the listing on the data set homepage and explain what can be expected for the following experiment.

2)Implement and document a multi-layer perceptron and the backpropagation training algorithm in Matlab (20%)

1)Build your code up systematically step by step and test. Provide evidence of that process.

2)5 of the 20 marks in this section are reserved as speed bonus for who can live-demo a MLP successfully learning XOR (or a similar function) by the 14/11/19.

3)Realize and describe an experiment in Matlab that evaluates the classification error rate for MLP on the MNIST dataset. Use appropriate illustrations and diagrams as well as statistics. (20%)

1)Make sure you have one successfully learning parameter set first, and start to explore systematically from there. Pay particular attention to finding an appropriate learning rate first.

2)This experiment can be conducted without a full back-propagation implementation as long as the forward propagation and the learning of the output layer works, although results will vary from the intended experiment.

4)Bonus points for additional features of MLP or experiment, see above. (10%)

5)Write a brief conclusion on the results and compare to results documented for other algorithms as well as MLP configurations on the data set homepage. Explain possible current limitations of your solutions and possible further strategies to improve on the results (10%)