Multi-Class Deep Learning Model for MNIST Digits Using PyTorch

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The MNIST Database of handwritten digits is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.

Additional Notes: This is a replication, with some small modifications, of Dr. Jason Brownlee’s blog post, PyTorch Tutorial: How to Develop Deep Learning Models with Python (https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/). I plan to leverage Dr. Brownlee’s tutorial code and build a PyTorch-based notebook template for future uses.

INTRODUCTION: The MNIST problem is a dataset developed by Yann LeCun, Corinna Cortes, and Christopher Burges for evaluating machine learning models on the handwritten digit classification problem. The dataset was constructed from many scanned document datasets available from the National Institute of Standards and Technology (NIST). The MNIST handwritten digit classification problem has become a standard dataset used in computer vision and deep learning.

Images of digits were taken from a variety of scanned documents, normalized in size and centered. Each image is a 28 by 28-pixel square (784 pixels total). A standard split of the dataset is used to evaluate and compare models, where 60,000 images are used to train a model, and a separate set of 10,000 images are used to test it. It is a digit recognition task, so there are ten classes (0 to 9) to predict.

ANALYSIS: After setting up the deep learning model, the model processed the test dataset with an accuracy measurement of 98.98%.

CONCLUSION: For this dataset, the model built using PyTorch achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: The MNIST Database of Handwritten Digits

Dataset ML Model: Multi-class classification with numerical attributes

Dataset Reference: http://yann.lecun.com/exdb/mnist/

One potential source of performance benchmarks: https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/

The HTML formatted report can be found here on GitHub.

Regression Deep Learning Model for Boston Housing Using PyTorch

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Boston Housing dataset is a regression situation where we are trying to predict the value of a continuous variable.

Additional Notes: This is a replication, with some small modifications, of Dr. Jason Brownlee’s blog post, PyTorch Tutorial: How to Develop Deep Learning Models with Python. I plan to leverage Dr. Brownlee’s tutorial code and build a PyTorch-based notebook template for future uses.

INTRODUCTION: The purpose of the analysis is to predict the housing values (thousands of dollar) in the suburbs of Boston by using the home sale transaction history.

ANALYSIS: After setting up the deep learning model, the model processed the test dataset with a root mean squared error (RMSE) of 8.906.

CONCLUSION: For this dataset, the model built using PyTorch achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: Boston Housing Dataset

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data

One potential source of performance benchmarks: https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/

The HTML formatted report can be found here on GitHub.

Multi-Class Deep Learning Model for Iris Flowers Using PyTorch

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Iris Flowers dataset is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.

Additional Notes: This is a replication, with some small modifications, of Dr. Jason Brownlee’s blog post, PyTorch Tutorial: How to Develop Deep Learning Models with Python. I plan to leverage Dr. Brownlee’s tutorial code and build a PyTorch-based notebook template for future uses.

INTRODUCTION: This is perhaps the best-known database to be found in the pattern recognition literature. Fisher’s paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains three classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other two; the latter are NOT linearly separable from each other.

ANALYSIS: After setting up the deep learning model, the model processed the test dataset with an accuracy measurement of 84.00%.

CONCLUSION: For this dataset, the model built using PyTorch achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: Iris Flowers Dataset

Dataset ML Model: Multi-class classification with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/

One potential source of performance benchmarks: https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/

The HTML formatted report can be found here on GitHub.

Binary Classification Deep Learning Model for Ionosphere Signals Using PyTorch

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Ionosphere Signals dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.

Additional Notes: This is a replication of Dr. Jason Brownlee’s blog post, PyTorch Tutorial: How to Develop Deep Learning Models with Python. I plan to leverage Dr. Brownlee’s tutorial code and build a PyTorch-based notebook template for future uses.

INTRODUCTION: The dataset contains radar data collected by a system that is consists of a phased array of 16 high-frequency antennas. The targets were free electrons in the ionosphere. The system processed the signals using an autocorrelation function. Instances in this dataset are described by two attributes per pulse number, corresponding to the complex values returned by the function resulting from the electromagnetic signal. The “Good” radar returns are those showing evidence of some type of structure in the ionosphere. The “Bad” labels are those that do not as their signals pass through the ionosphere.

ANALYSIS: After setting up the deep learning model, the model processed the test dataset with an accuracy measurement of 87.93%.

CONCLUSION: For this dataset, the model built using PyTorch achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: Ionosphere Signals Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Ionosphere

One potential source of performance benchmarks: https://machinelearningmastery.com/pytorch-tutorial-develop-deep-learning-models/

The HTML formatted report can be found here on GitHub.