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Java Technical Lead, Tech Lead, NLP, Machine Learning, AWS, ElasticSearch, REST APIs - JY/4087

#artificialintelligence

Java Development Lead, Team Lead, NLP, Machine Learning, AWS, ElasticSearch, REST APIs My industry leading global client is looking for a Java Technical Lead / Java Team Lead for a permanent position based in their Oxford offices. This an extremely exciting opportunity to work with some of the world's best technologists using cutting edge technologies, Machine Learning and Natural Language Processing to make radical advancements. As a lead you'll have diverse responsibilities, including hands on development, design, code reviews, mentoring of more junior team members and process improvement. Key Responsibilities of the Java Technical Lead / Software Engineering Lead: • Implement new features in our system from initial design through delivery • Work with users and product management to define what they want, what they need, and what we can deliver • Find opportunities for continuous improvements to our system • Fix issues and rework code, monitors, and alerts for high stability • Learn and apply best practices across the entire stack • Be part of the team • Interfacing with on and offshore teams • Providing technical direction and peer leadership • Monitoring, steering and advising both on and offshore development work The Java Technical Lead / Software Engineering Lead will bring skills/experience in: • Strong Java skills. You are a Java programmer and have stayed current with the evolution of the Java language and its ecosystem of frameworks and build tools.


DeepTriage

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For a given software bug report, identifying an appropriate developer who could potentially fix the bug is the primary task of a bug triaging process. A bug title (summary) and a detailed description is present in most of the bug tracking systems. Automatic bug triaging algorithm can be formulated as a classification problem, which takes the bug title and description as the input, mapping it to one of the available developers (class labels). The major challenge is that the bug description usually contains a combination of free unstructured text, code snippets, and stack trace making the input data highly noisy. In the past decade, there has been a considerable amount of research in representing a bug report using tf-idf based bag-of-words feature (BOW) model. However, BOW model do not consider the syntactical and sequential word information available in the descriptive sentences.


Intro to text classification with Keras: automatically tagging Stack Overflow posts Google Cloud Big Data and Machine Learning Blog Google Cloud Platform

@machinelearnbot

Posted by Sara Robinson (Developer Advocate), Josh Gordon (Developer Advocate), and Marianne Linhares Monteiro (DA Intern). As humans, our brains can easily read a piece of text and extract the topic, tone, and sentiment. Up until just a few years ago, teaching a computer to do the same thing required extensive machine learning expertise and access to powerful computing resources. Now, frameworks like TensorFlow are helping to simplify the process of building machine learning models, and making it more accessible to developers with no background in ML. In this post, we'll show you how to build a simple model to predict the tag of a Stack Overflow question.


Why Machine Learning Is Key to the Search Marketing of Tomorrow

@machinelearnbot

Advertising has changed a lot over the years. There was a time when machine learning, automation, and software-based marketing tech stacks weren't a "thing." There are hundreds of channels across physical and print media and online at present, including social, mobile, and video. Even TV has diversified into hundreds of cable channels on your remote control. And yet, digital ad revenue has gone on to surpass that of TV.


Operationalizing Data Science Models on the Pivotal Stack

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At Pivotal Data Science, our primary charter is to help our customers derive value from their data assets, be it in the reduction of cost or by increasing revenue by offering better products and services. While we are not working on customer engagements, we engage in R&D using our wide array of products. For instance, we may contribute a new module to PDLTools or MADlib - our distributed in-database machine learning libraries, we might build end-to-end demos such as these or experiment with new technology and blog about them here. Last quarter, we set out to explore data science microservices for operationalizing our models for real-time scoring. Microservices have been the most talked about topic in many Cloud conferences of late. They've gained a large fan following by application developers, solution architects, data scientists and engineers alike.


Word vectors for non-NLP data and research people – Conor McDonald – Medium

#artificialintelligence

We create a list of words, apply spaCy's parser, extract the vector for each word, stack them together and then extract two-principal components for visualisation purposes.


Metis - Data Science Training

#artificialintelligence

You have been cruelly misled into thinking that the way to get a data science job is to stack up pre-requisites and hope somebody picks you. You have wasted your very limited time and energy trying to keep up with the ever growing heap of data science "must-knows" like Machine Learning Algorithms, Distributed Computing, SQL & NoSQL Databases, Statistical Modeling, Deep Learning, Natural Language Processing, Data Visualization, Hadoop, Kafka, Spark, Big Data, and more. Your patience, confidence, and sleep are plummeting from the fear that you'll chose the wrong things to learn and you won't get a data science job. How would you feel waking up tomorrow knowing how to get the exact type of data science job that fulfills all of your goals? You would be overflowing with extra energy and time because of your ability to make all of the right choices of what to learn, what to study, and what to ignore.


Artificial Intelligence Is the Future of Everything, Especially E-Commerce

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E-commerce retail is abuzz about artificial intelligence (AI), and for good reason. The artificial intelligence market is projected to be worth over $16 billion by 2022, and the retail sector is one of the main drivers of growth. Some of the largest retailers have entered a tech arms race -- acquiring startups and building their own tech stacks. It's official: Retail has gone all in on marketing technology. AI-powered assistants and beyond, how will e-commerce marketers and consumers benefit from artificial intelligence?


AI Programming: So Much Uncertainty - The New Stack

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Much work, and many tools, are still needed to integrate artificial intelligence into the software engineering workflow, noted Peter Norvig, Google's director of research, speaking at the O'Reilly Artificial Intelligence conference in New York last week. Fundamentally, AI software is inherently different from other forms of widely used software, said Norvig, who is also a co-author of perhaps the most popular book of programming instruction for the field, Artificial Intelligence: A Modern Approach. "One way of looking at the traditional model of programming is to look at the programmer is a micro-manager, who tells a computer exactly how to do something step by step," he said. With AI, we should look at the programmer more as a teacher, rather than a micro-manager. This will require big changes in how programming is done, and the tools needed to program easily.


Top 15 Python Libraries for Data Science in 2017

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It is rather a web-based toolbox for building visualizations, exposing APIs to some programming languages (Python among them). There is a number of robust, out-of-box graphics on the plot.ly In order to use Plotly, you will need to set up your API key. The graphics will be processed server side and will be posted on the internet, but there is a way to avoid it. Scikits are additional packages of SciPy Stack designed for specific functionalities like image processing and machine learning facilitation.