Author: Jason Brownlee Generative adversarial networks, or GANs for short, are an effective deep learning approach for developing generative models. Unlike other deep learning neural […] Read More
Author: Charles Durfee
Computer Science student with a passion for data science and artificial intelligence.
Author: Stephanie Glen This simple picture shows the differences between descriptive statistics and Inferential statistics. More information about Descriptive and Inferential Statistics: What is “Descriptive […] Read More
Author: mtdearing Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch […] Read More
Author: Vincent Granville I introduce here a family of very peculiar statistical distributions governed by two parameters: p, a real number in [0, 1], and […] Read More
Author: Matt Mayo Editor Check out this eBook, How to Enhance Privacy in Data Science, to equip yourself with the tools to enhance privacy in […] Read More
Author: Olha Zhydik Neural networks have made significant leaps in the image and natural language processing (NLP) recently. They’ve not only learned to recognise, localise and segment […] Read More
Author: The Amazing Ways YouTube Uses Artificial Intelligence And Machine Learning Forbes There are more than 1.9 billion users logged in to YouTube every single month […] Read More
Author: Jason Brownlee Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating […] Read More
Author: Matt Mayo Editor Also: For data pros only – An SQL Query walks into a bar and sees two tables; Deep Learning for NLP: […] Read More
Three-way data splits (training, test and validation) for model selection and performance estimation
Author: ajit jaokar The use of training, validation and test datasets is common but not easily understood. In this post, I attempt to clarify […] Read More