Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.
In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.
In this post you will find a list of common the data fallacies that lead to incorrect conclusions and poor decision-making using data. Here you will find great resources and information so that you can always be reminded of these fallacies when you’re working with data.
We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.
Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.
We compare survival analysis to other predictive techniques, and provide examples of how it can produce business value, with a focus on Kaplan-Meier and Cox Regression methods which have been underutilized in business analytics.
We introduce a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.
Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras