10/22/2023 0 Comments Matlab for machine learningLooking at the next runners-up, googlenet might be a better compromise since it was much faster and still has similarly good validation accuracy (91%). We found that in this example, inceptionv3 performed best in terms of accuracy (91.9 %) but takes much longer as it’s a more complicated architecture compared to the others. Most of these measures can be quickly found in the app - more on explainability below, as it's much more nuanced!įig 5: Classification results in Experiment Manager App How did our models perform? There are a few criteria we used to assess: I started the experiment a bit early to ensure we had time to compare and ran it on my Linux machine for multi-GPU action! You can adjust setting to use GPUs and run experiments in parallel easily through the app. This doc example walks through setting up and running the experiment:Ĭd(setupExample('nnet/ExpMgrTransferLearningExample')) setupExpMgr('FlowerTransferLearningProject') Fig 4: Setting network parameters in Experiment Manager AppĪs you probably know, training networks can take some time! Here we are training 3 of them - so you want to consider your hardware and problem before hitting run. Next, we needed to train and validate all 3 networks and compare the results! The Experiment Manager app is super helpful to stay organized and automate this part. We wanted varying levels of complexity for our competition, so we decided on squeezenet, googlenet, and inceptionv3. We started by exploring pretrained models using the Deep Network Designer app which provides a sense of the overall network architecture to help us select before investigating the detail.įig 3: Pretrained models in Deep Network Designer CNNs are very common as they involve a series of operations, which we can generally understand: convolutions, mathematical operations, and aggregations.įig 2: Convolutional Neural Network (CNN) diagramĪs you may recall from previous posts, we have some great starting points in this field! We used transfer learning, where you update a pretrained network with your data. We'll discuss more as we get into the details!įor our first problem, we compared CNN models to classify types of flowers. We used doc examples for repeatability (plus, reasonably sized data sets for a livestream!) and used apps in MATLAB to explore, train, and compare the models quickly. The image below shows some common networks used for different data types. Based on the data sets, we considered two types of models: Convolutional (CNN) and Long Short-Term Memory (LSTM) networks. We created two problems for image classification and timeseries regression. How can this be managed efficiently and quickly? Using a low code tool in MATLAB, the Experiment Manager app! Approach It could be a) comparing different networks (problem 1) or b) finding the right parameters for a particular network (problem 2). For deep learning models, there are different ways to assess what is the “best” model. This blog post follows the fabulous modeling competition LIVE on YouTube, MATLAB's Best Model: Deep Learning Basics to guide you in how to choose the best model. You can follow her on social media:, ,, and. Many toolbox algorithms can be used on data sets that are too big to be stored in memory.This post is from Heather Gorr, MATLAB product marketing. Native Simulink blocks let you use predictive models with simulations and Model-Based design. You can apply interpretability techniques such as partial dependence plots, Shapley values and LIME, and automatically generate C/C++ code for embedded deployment. The toolbox provides supervised, semi-supervised, and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted decision trees, shallow neural nets, k-means, and other clustering methods. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.įor multidimensional data analysis and feature extraction, the toolbox provides principal component analysis (PCA), regularization, dimensionality reduction, and feature selection methods that let you identify variables with the best predictive power. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis fit probability distributions to data generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data.
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