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Moving machine learning from practice to production

#artificialintelligence

With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Coupled with breakneck speeds in AI-research, the new wave of popularity shows a lot of promise for solving some of the harder problems out there. That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks. A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved. This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production.


Time Series Prediction With Deep Learning in Keras - Machine Learning Mastery

#artificialintelligence

Time Series prediction is a difficult problem both to frame and to address with machine learning. In this post you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. The problem we are going to look at in this post is the international airline passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. Below is a sample of the first few lines of the file.


Time Series Prediction With Deep Learning in Keras - Machine Learning Mastery

#artificialintelligence

Time Series prediction is a difficult problem both to frame and to address with machine learning. In this post you will discover how to develop neural network models for time series prediction in Python using the Keras deep learning library. The problem we are going to look at in this post is the international airline passengers prediction problem. This is a problem where given a year and a month, the task is to predict the number of international airline passengers in units of 1,000. Below is a sample of the first few lines of the file.


Next Word Prediction with NLP and Deep Learning

#artificialintelligence

This section will cover what the next word prediction model built will exactly perform. The model will consider the last word of a particular sentence and predict the next possible word. We will be using methods of natural language processing, language modeling, and deep learning. We will start by analyzing the data followed by the pre-processing of the data. We will then tokenize this data and finally build the deep learning model.


Next Generation Applied Artificial Intelligence

#artificialintelligence

The proliferation of data within the military poses a significant challenge for operators interpreting differing data sets into meaningful information upon which to make informed and timely decisions. Artificial Intelligence capabilities are developing at pace and can present opportunities through Deep Learning for operators and decision makers to interpret vast, disparate data sets concurrently. The Defence and Security Accelerator (DASA) has funded two projects led by Montvieux, in excess of £500,000, following a DASA themed competition to find new technologies, processes and ideas to'Revolutionise the human information relationship for Defence'. The Prediction Toolset is a Deep Learning based Artificial Intelligence capability that uses current and historical information to predict the change of control on the ground, in both space and time, between opposing groups fighting within an operational theatre. This capability provides foresight to analysts and collection managers, enabling them to proactively anticipate future events on the ground, thereby enhancing the protection of forces and improving the efficiency and effectiveness of information collection.