How to Evaluate Generative Adversarial Networks

Generative adversarial networks, or GANs for short, are an effective deep learning approach for developing generative models. Unlike other deep learning neural network models that are trained with a loss function until convergence, a GAN generator model is trained using a second model called a discriminator that learns to classify images as real or generated. Both the generator and discriminator model are trained together to maintain an equilibrium. As such, there is no objective loss function used to train the […]

September 29, 2020 Machine Learning

How to Implement the Inception Score (IS) for Evaluating GANs

Last Updated on October 11, 2019 Generative Adversarial Networks, or GANs for short, is a deep learning neural network architecture for training a generator model for generating synthetic images. A problem with generative models is that there is no objective way to evaluate the quality of the generated images. As such, it is common to periodically generate and save images during the model training process and use subjective human evaluation of the generated images in order to both evaluate the […]

September 29, 2020 Machine Learning

How to Implement the Frechet Inception Distance (FID) for Evaluating GANs

Last Updated on October 11, 2019 The Frechet Inception Distance score, or FID for short, is a metric that calculates the distance between feature vectors calculated for real and generated images. The score summarizes how similar the two groups are in terms of statistics on computer vision features of the raw images calculated using the inception v3 model used for image classification. Lower scores indicate the two groups of images are more similar, or have more similar statistics, with a […]

September 29, 2020 Machine Learning

A Gentle Introduction to Generative Adversarial Network Loss Functions

The generative adversarial network, or GAN for short, is a deep learning architecture for training a generative model for image synthesis. The GAN architecture is relatively straightforward, although one aspect that remains challenging for beginners is the topic of GAN loss functions. The main reason is that the architecture involves the simultaneous training of two models: the generator and the discriminator. The discriminator model is updated like any other deep learning neural network, although the generator uses the discriminator as […]

September 29, 2020 Machine Learning

A Gentle Introduction to Jensen’s Inequality

Last Updated on July 31, 2020 It is common in statistics and machine learning to create a linear transform or mapping of a variable. An example is a linear scaling of a feature variable. We have the natural intuition that the mean of the scaled values is the same as the scaled value of the mean raw variable values. This makes sense. Unfortunately, we bring this intuition with us when using nonlinear transformations of variables where this relationship no longer […]

September 29, 2020 Machine Learning

How to Develop and Evaluate Naive Classifier Strategies Using Probability

Last Updated on September 25, 2019 A Naive Classifier is a simple classification model that assumes little to nothing about the problem and the performance of which provides a baseline by which all other models evaluated on a dataset can be compared. There are different strategies that can be used for a naive classifier, and some are better than others, depending on the dataset and the choice of performance measures. The most common performance measure is classification accuracy and common […]

September 29, 2020 Machine Learning

Resources for Getting Started With Probability in Machine Learning

Last Updated on September 25, 2019 Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and linear algebra, probability is another foundational field that supports machine learning. Probability is a field of mathematics concerned with quantifying uncertainty. Many aspects of machine learning are uncertain, including, most critically, observations from the problem domain and the relationships learned by models from that data. As such, some understanding of probability and tools and […]

September 29, 2020 Machine Learning

5 Reasons to Learn Probability for Machine Learning

Last Updated on November 8, 2019 Probability is a field of mathematics that quantifies uncertainty. It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior to getting started. This is misleading advice, as probability makes more sense to a practitioner once they have the context of the applied machine learning process in which to interpret it. In this post, you will discover why machine learning practitioners should study […]

September 29, 2020 Machine Learning

A Gentle Introduction to Uncertainty in Machine Learning

Last Updated on September 25, 2019 Applied machine learning requires managing uncertainty. There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the sample of data collected from the domain, and in the imperfect nature of any models developed from such data. Managing the uncertainty that is inherent in machine learning for predictive modeling can be achieved via the tools and techniques from probability, a field specifically designed to handle uncertainty. In […]

September 29, 2020 Machine Learning

What Is Probability?

Last Updated on November 29, 2019 Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty is typically described using everyday words like chance, luck, and risk. Probability is a field of mathematics that gives us the language and tools to quantify the uncertainty of events and reason in a principled manner. In this post, you will discover a gentle introduction to probability. After reading this post, you will know: […]

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