Inpainting and outpainting have long been popular and well-studied image processing domains. Traditional approaches to these problems often relied on complex algorithms and deep learning techniques yet still gave inconsistent outputs. However, recent advancements in the form of Stable diffusion have reshaped these domains. Stable diffusion now offers enhanced efficacy in inpainting and outpainting while […]
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MOSTLY AI: The most accurate synthetic data generator
Sponsored Post By Georgios Loizou, AI & Machine Learning Product Owner at MOSTLY AI Update: SDV changed their license model in 2023, and is NOT open-source anymore. As businesses attempt to extract relevant insights and build powerful machine-learning models, the need for high-quality, accurate synthetic datasets has grown. MOSTLY AI is excited […]
How do you generate synthetic data for machine learning and why do you need it?
Sponsored Post Engineers all over the globe get instant headaches and feel seriously unwell when they hear the “Data is the new oil” phrase. Well, if it is, then why don’t we just go to the nearest data pump and fill up our tanks for a nice, long ride down machine learning valley? […]
What Is Semi-Supervised Learning
Semi-supervised learning is a learning problem that involves a small number of labeled examples and a large number of unlabeled examples. Learning problems of this type are challenging as neither supervised nor unsupervised learning algorithms are able to make effective use of the mixtures of labeled and untellable data. As such, specialized semis-supervised learning algorithms […]
14 Different Types of Learning in Machine Learning
Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence. The focus of the field is learning, that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data. As such, there are many different types of […]
How to Calculate the KL Divergence for Machine Learning
It is often desirable to quantify the difference between probability distributions for a given random variable. This occurs frequently in machine learning, when we may be interested in calculating the difference between an actual and observed probability distribution. This can be achieved using techniques from information theory, such as the Kullback-Leibler Divergence (KL divergence), or […]
How to Develop a Naive Bayes Classifier from Scratch in Python
Classification is a predictive modeling problem that involves assigning a label to a given input data sample. The problem of classification predictive modeling can be framed as calculating the conditional probability of a class label given a data sample. Bayes Theorem provides a principled way for calculating this conditional probability, although in practice requires an […]
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 […]
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. […]
A Gentle Introduction to BigGAN the Big Generative Adversarial Network
Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results. The BigGAN is an approach to pull together a suite of recent best […]