Matlab Neural Network Tutorial

[Mohammed Huberty]

Forexample, you could create a network with more hidden layers, or a deep neural network. There are many types of deep networks supported in MATLAB and resources for deep learning. For more info, check out the links in the description below.

You can generate a MATLAB function or Simulink diagram for simulating your neural network. Use genfunction to create the neural network including all settings, weight and bias values, functions, and calculations in one MATLAB function file.

Deep Learning Onramp

This free, two-hour deep learning tutorial provides an interactive introduction to practical deep learning methods. You will learn to use deep learning techniques in MATLAB for image recognition.

Interactively Modify a Deep Learning Network for Transfer Learning

Deep Network Designer is a point-and-click tool for creating or modifying deep neural networks. This video shows how to use the app in a transfer learning workflow. It demonstrates the ease with which you can use the tool to modify the last few layers in the imported network as opposed to modifying the layers in the command line. You can check the modified architecture for errors in connections and property assignments using a network analyzer.

Deep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code

Learn how to use transfer learning in MATLAB to re-train deep learning networks created by experts for your own data or task.

Deep Learning is a very hot topic these days especially in computer vision applications and you probably see it in the news and get curious. Now the question is, how do you get started with it? Today's guest blogger, Toshi Takeuchi, gives us a quick tutorial on artificial neural networks as a starting point for your study of deep learning.

Many of us tend to learn better with a concrete example. Let me give you a quick step-by-step tutorial to get intuition using a popular MNIST handwritten digit dataset. Kaggle happens to use this very dataset in the Digit Recognizer tutorial competition. Let's use it in this example. You can download the competition dataset from "Get the Data" page:

The first column is the label that shows the correct digit for each sample in the dataset, and each row is a sample. In the remaining columns, a row represents a 28 x 28 image of a handwritten digit, but all pixels are placed in a single row, rather than in the original rectangular form. To visualize the digits, we need to reshape the rows into 28 x 28 matrices. You can use reshape for that, except that we need to transpose the data, because reshape operates by column-wise rather than row-wise.

The dataset stores samples in rows rather than in columns, so you need to transpose it. Then you will partition the data so that you hold out 1/3 of the data for model evaluation, and you will only use 2/3 for training our artificial neural network model.

W in the diagram stands for weights and b for bias units, which are part of individual neurons. Individual neurons in the hidden layer look like this - 784 inputs and corresponding weights, 1 bias unit, and 10 activation outputs.

If you look inside myNNfun.m, you see variables like IW1_1 and x1_step1_keep that represent the weights your artificial neural network model learned through training. Because we have 784 inputs and 100 neurons, the full layer 1 weights will be a 100 x 784 matrix. Let's visualize them. This is what our neurons are learning!

Now you are ready to use myNNfun.m to predict labels for the heldout data in Xtest and compare them to the actual labels in Ytest. That gives you a realistic predictive performance against unseen data. This is also the metric Kaggle uses to score submissions.

First, you see the actual output from the network, which shows the probability for each possible label. You simply choose the most probable label as your prediction and then compare it to the actual label. You should see 95% categorization accuracy.

You probably noticed that the artificial neural network model generated from the Pattern Recognition Tool has only one hidden layer. You can build a custom model with more layers if you would like, but this simple architecture is sufficient for most common problems.

The next question you may ask is how I picked 100 for the number of hidden neurons. The general rule of thumb is to pick a number between the number of input neurons, 784 and the number of output neurons, 10, and I just picked 100 arbitrarily. That means you might do better if you try other values. Let's do this programmatically this time. myNNscript.m will be handy for this - you can simply adapt the script to do a parameter sweep.

As you can see, you gain more accuracy if you increase the number of hidden neurons, but then the accuracy decreases at some point (your result may differ a bit due to random initialization of weights). As you increase the number of neurons, your model will be able to capture more features, but if you capture too many features, then you end up overfitting your model to the training data and it won't do well with unseen data. Let's examine the learned weights with 300 hidden neurons. You see more details, but you also see more noise.

You now have some intuition on artificial neural networks - a network automatically learns the relevant features from the inputs and generates a sparse representation that maps to the output labels. What if we use the inputs as the target values? That eliminates the need for training labels and turns this into an unsupervised learning algorithm. This is known as an autoencoder and this becomes a building block of a deep learning network. There is an excellent example of autoencoders on the Training a Deep Neural Network for Digit Classification page in the Deep Learning Toolbox documentation, which also uses MNIST dataset. For more details, Stanford provides an excellent UFLDL Tutorial that also uses the same dataset and MATLAB-based starter code.

Beyond understanding the algorithms, there is also a practical question of how to generate the input data in the first place. Someone spent a lot of time to prepare the MNIST dataset to ensure uniform sizing, scaling, contrast, etc. To use the model you built from this dataset in practical applications, you have to be able to repeat the same set of processing on new data. How do you do such preparation yourself?

There is a fun video that shows you how you can solve Sudoku puzzles using a webcam that uses a different character recognition technique. Instead of static images, our colleague Teja Muppirala uses a live video feed in real time to do it and he walks you through the pre-processing steps one by one. You should definitely check it out: Solving a Sudoku Puzzle Using a Webcam.

You got 96% categorization accuracy rate by simply accepting the default settings except for the number of hidden neurons. Not bad for the first try. Since you are using a Kaggle dataset, you can now submit your result to Kaggle.

Learn the basics of deep learning for image classification problems in MATLAB. Use a deep neural network that experts have trained and customize the network to group your images into predefined categories.

I tried last time to work by python, but I found that I need long time to learn python from scratch. We are using Matlab for almost everything and I master it well. I decided to move back to matlab to use deep learning there,. my question, do anyone have any videos or tutorial I can follow to learn how deep learning in Matlab?

Deep learning in layman term is a neural net with lots of neurons in many (deep) layers. Deep Learning Toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks.

If you need to install the webcam and alexnet add-ons, a message from each function appears with a link to help you download the free add-ons using Add-On Explorer. Alternatively, see Deep Learning Toolbox Model for AlexNet Network and MATLAB Support Package for USB Webcams.After you install Deep Learning Toolbox Model for AlexNet Network, you can use it to classify images. AlexNet is a pre-trained convolutional neural network (CNN) that has been trained on more than a million images and can classify images into 1000 object categories (for example, keyboard, mouse, coffee mug, pencil, and many animals).

MATLAB is a high-level language and interactive environment designed for numerical computation, visualization, and programming. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.

MATLAB stands for Matrix Laboratory, reflecting its strength in matrix operations, which are critical in scientific and engineering tasks. It's used widely in academia and industry for a range of applications, including video and image processing, control systems, signal processing and communication, testing and measurements, computational biology, and computational finance.

MATLAB provides a simple syntax and desktop environment tuned for iterative analysis and design processes, leading to a much shorter learning curve compared to other programming languages. Moreover, its extensive library of pre-built functions allows users to create sophisticated programs without having to be an expert programmer.

Machine learning is a method of data analysis that automates analytical model building. It's a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. MATLAB has several benefits for machine learning applications:

One of the key functions for MATLAB machine learning is fitcsvm. This function is used to train a Support Vector Machine (SVM) classifier for binary classification. SVMs are powerful models that can handle both linear and non-linear classification tasks. They work by finding the hyperplane that best separates the classes in the feature space.

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