How telecom providers are embracing cognitive app development


As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. Machine learning can deliver benefits across telecom provider operations with financially-oriented applications - including fraud mitigation and revenue assurance - which currently make the most compelling use cases. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization.

Model evaluation, model selection, and algorithm selection in machine learning


In contrast to k-nearest neighbors, a simple example of a parametric method would be logistic regression, a generalized linear model with a fixed number of model parameters: a weight coefficient for each feature variable in the dataset plus a bias (or intercept) unit. While the learning algorithm optimizes an objective function on the training set (with exception to lazy learners), hyperparameter optimization is yet another task on top of it; here, we typically want to optimize a performance metric such as classification accuracy or the area under a Receiver Operating Characteristic curve. Thinking back of our discussion about learning curves and pessimistic biases in Part II, we noted that a machine learning algorithm often benefits from more labeled data; the smaller the dataset, the higher the pessimistic bias and the variance -- the sensitivity of our model towards the way we partition the data. We start by splitting our dataset into three parts, a training set for model fitting, a validation set for model selection, and a test set for the final evaluation of the selected model.

Machine Learning and Visualization in Julia – Tom Breloff


In this post, I'll introduce you to the Julia programming language and a couple long-term projects of mine: Plots for easily building complex data visualizations, and JuliaML for machine learning and AI. Easily create strongly-typed custom data manipulators. "User recipes" and "type recipes" can be defined on custom types to enable them to be "plotted" just like anything else. We believe that Julia has the potential to change the way researchers approach science, enabling algorithm designers to truly "think outside the box" (because of the difficulty of implementing non-conventional approaches in other languages).

Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow


A positive label means that an utterance was an actual response to a context, and a negative label means that the utterance wasn't – it was picked randomly from somewhere in the corpus. Each record in the test/validation set consists of a context, a ground truth utterance (the real response) and 9 incorrect utterances called distractors. Before starting with fancy Neural Network models let's build some simple baseline models to help us understand what kind of performance we can expect. The Deep Learning model we will build in this post is called a Dual Encoder LSTM network.

How to get the most out of machine learning systems


Focusing on supervised machine learning, there is a task at hand and there is labelled data to train a machine learning system. This applies to many highly specialised domains where data is unique and, in fact, the importance of data modelling and subject matter experts is something that should not be overlooked. The phrase'garbage in, garbage out' has almost become synonymous with the world of supervised machine learning systems. By creating a domain/data model and utilising subject matter experts throughout the development process, the machine learning system will almost certainly guarantee success.

Behavior I/O: Using Machine Learning to Empower Human Learning


You can think of this virtuous cycle as "Behavior I/O:" In the consumer world, many companies like Fitbit (NYSE: FIT) and LinkedIn (NYSE: LNKD) learn from people's behavior to help train other people to behave better. Could you use machine learning to observe behavior of analysts, ultimately using those observations to improve how their colleagues use data? Even with such a rich corpus, there are still a lot of problems to solve in implementing a behavioral learning system that actually helps drive behavior. Before Alation, Satyen spent nearly a decade at Oracle, ultimately running the Financial Services Warehousing and Performance Management business where he helped customers get insights out of their systems.

What Is Deep Learning? A Short History Everyone Should Read


Scientists have used deep learning algorithms with multiple processing layers (hence "deep") to make better models from large quantities of unlabeled data (such as photos with no description, voice recordings or videos on YouTube). Google's voice recognition algorithms operate with a massive training set -- yet it's not nearly big enough to predict every possible word or phrase or question you could put to it. And Google's deep learning algorithm discovers cats. Algorithms perform superior face recognition tasks using deep network that take into account 120 million parameters.

Google developed a processor to power its AI bots


Machine learning, which helps computers do things like understand complex voice commands and improve image search capabilities, can be taxing on traditional hardware. The Tensor Processing Unit (TPU) is built expressly for running TensorFlow, Google's in-house machine learning system that it open-sourced last year. Google says it's used TPUs in its data centers for more than a year now, and that the performance improvements they offer are roughly equivalent to fast-forwarding technology about seven years into the future, if you go by Moore's Law. However, you can be sure that these processors will be at the heart of Google's forthcoming technologies and services as AI and machine learning become more important in the future.

Ping pong and robotics: A match made in heaven


Enter Trainerbot, the smart ping pong robot with a wicked serve. Harrison started working on a ping pong robot made from a household garbage can. Puma has developed a racing robot to push runners, with the idea that competing against an opponent helps improve athletes' performance. For a totally customizable game, users can control the motors via a mobile app.

Distributed Deep Learning with Caffe Using a MapR Cluster


Google, Baidu, and Microsoft have the resources to build dedicated deep learning clusters that give the deep learning algorithms a level of processing power that both accelerates training time as well as increases their model's accuracy. Yahoo, however, has taken a slightly different approach, by moving away from a dedicated deep learning cluster and combining Caffe with Spark. The ML Big Data team's CaffeOnSpark software has allowed them to run the entire process of building and deploying a deep learning model onto a single cluster. The MapR Converged Data Platform is the ideal platform for this project, giving you all the power of distributed Caffe on a cluster with enterprise-grade robustness, enabling you to take advantage of the MapR high performance file system.