We'll be live streaming both the events on YouTube, so if you aren't able to make it, do watch the live streams (YouTube lets you set a reminder): The Fifth Elephant and Anthill Inside expose you to trends in data science, deep learning and artificial intelligence. Let's walk through the schedules for both events: At this point, we'd like to make a special mention about our diversity sponsor -- Intuit India -- for sponsoring child care facilities at The Fifth Elephant, and Anthill Inside. We'd like to talk about community for women and non-binary gender data scientists, the problems we are solving in the field, and how we can foster more diversity in data science. On that note, Intel has created a developer portal for ML engineers, data scientists and students with resources on optimized frameworks, and training for artificial intelligence, machine learning, and deep learning.
This week at Black Hat, one researcher hopes to contribute to the discipline by showing off a new automated AI agent that probes the data science behind machine learning malware detection models and looks for mathematical weaknesses. "All machine learning models have blind spots. The agent essentially inspects an executable file and uses a sequence of file mutations to test the detection model. The idea of machine learning and AI hardening is generally gaining momentum with data scientists and security specialists of late.
That's why we invited Igor Mikhalev from Firmshift, a data-driven technology development company, to answer a few questions about machine learning and AI. However, talking about short to mid-term, I believe the focus will be on the ownership, sufficiency, and readiness of data, as well as organizational capabilities to nurture the creative process of working with internal and external data in the context of cross-functional business (model) innovation, supported by machine learning technology. Once you've established first results, build awareness and a clear business case to establish data science competency, and work with HR to start nurturing a data-driven culture that underpins its importance and usefulness. Designing AI would entail helping business, science, and engineering teams to think creatively, drive the cross-functional business (model) innovation process, challenge conventional wisdom, and become aware of the differences imposed by each other's thinking realms.
Using algorithms that learn iteratively, machine learning lets you discover hidden insight from your data. It's a simple idea with phenomenal impact and sophisticated use cases like recommenders, text mining, real-time analytics, large-scale anomaly detection, and business forecasting. Strata is a unique opportunity to get up to speed quickly on the latest in machine and deep learning. Take a look at the machine learning sessions available to you at Strata.
There's a lot of hype over machine learning and data science these days. Machine learning and data science strategies need to be well thought-out and planned. Machine learning can help you move past a generic authentication and access security analysis model, toward a user and entity behavioral analytics (UEBA) model. And it can help control costs by providing executive and product management the information they need to make informed, strategic business decisions.
Artificial neural networks are a type of machine learning, and take inspiration from neuron activity to solve problems that are too complex for traditional programming. As researchers move closer towards transformative artificial intelligence, cognitive will become increasingly relevant. Deep learning feeds data to a computer via artificial neural networks, aiming to solve any problem that requires thought. Machine learning methods include pattern recognition, natural language processing and data mining.
In this article, I will begin by covering fundamental principles, general process and types of problems in Data Science. In this article, I will begin by covering principles, general process and types of problems in Data Science. If the organization needs to grow our the customer base by targeting new segments and reducing customer churn, how can we decompose it into machine learning problems? Once we have defined the business problem and decomposed into machine learning problems, we need to dive deeper into the data.
Basic Artificial Intelligence: Learning a pattern from data with a single Machine Learning algorithm and fixed representation. Advanced Artificial Intelligence: Learning a pattern from data with a meta-learning algorithm applied to the space of Machine Learning algorithms and representations. The learning algorithm can be applied to hyper-parameter tuning, the type of Machine Learning, the problem representation or some combination of the three. My favorite Advanced Artificial Intelligence technique is Evolutionary Algorithms used to build Deep Learning networks.
Experienced teams know when to back up seeing a piling debt, but technical debt in machine learning piles extremely fast. Here are three fantastic papers that explore this issue: Machine Learning: The High Interest Credit Card of Technical Debt NIPS'14 Hidden Technical Debt in Machine Learning Systems NIPS'15 What's your ML test score? In small companies, it is relatively easy to control the feedback loops, but in large companies with dozens of teams working on dozens of complex systems piped into each other some of the feedback loops are very likely to be missed. With the feedback loops, your metrics won't reflect the real quality of the system and your ML model will learn to exploit these feedback loops instead of learning useful things.
Deep Learning: Google Now also known as Google Assistant on your Android smartphone, uses Deep Learning to perform tasks using large amounts of data. When you ask a question, the Google Assistant searches through large amounts of data to give you an answer. It solves real world problems by using neural networks to mimic human decision making. The machine understands how to mimic human decisions by training itself on large amounts of data.