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Introduction to QuantLib Development - Intensive 3-day Training Course - September 10-12th, 2018 - Download Registration Form Here

 

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The Practical Quant wrote a new blog post titled Assessing progress in automation technologies
[A version of this post appears on the O'Reilly Radar.]When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.In this post, I share slides and notes from a keynote Roger Chen and I gave at the Artificial Intelligence conference in London in October 2018. We presented an overview of the state of automation technologies: we tried to highlight the state of the key building block technologies and we described how these tools might evolve in the near future.To assess the state of adoption of machine learning (ML) and AI, we recently conducted a...
13 days ago
The Practical Quant wrote a new blog post titled Tools for generating deep neural networks with efficient network architectures
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning to see tools that enable data scientists and data engineers to scale and tackle many more problems...
13 days ago
The Practical Quant wrote a new blog post titled Building tools for enterprise data science
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science.In this episode of the Data Show, I spoke with Vitaly Gordon, VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people. We need automation tools for the many stages involved in data science, including data preparation, feature engineering, model selection and hyperparameter tuning,...
28 days ago
The Practical Quant wrote a new blog post titled Managing risk in machine learning
[A version of this post appears on the O'Reilly Radar.]Considerations for a world where ML models are becoming mission critical.In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.Let’s begin by looking at the state of adoption. We recently conducted a surveywhich garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. One of the things we learned...
36 days ago
The Practical Quant wrote a new blog post titled Lessons learned while helping enterprises adopt machine learning
[A version of this post appears on the O'Reilly Radar blog.]The O'Reilly Data Show Podcast: Francesca Lazzeri and Jaya Mathew on digital transformation, culture and organization, and the team data science process.In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep Learning” and “The State of Machine Learning Adoption in the Enterprise” — and we found that while many...
41 days ago
The Practical Quant wrote a new blog post titled Machine learning on encrypted data
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Alon Kaufman on the interplay between machine learning, encryption, and security.In this episode of the Data Show, I spoke with Alon Kaufman, CEO and co-founder of Duality Technologies, a startup building tools that will allow companies to apply analytics and machine learning to encrypted data. In a recent talk, I described the importance of data, various methods for estimating the value of data, and emerging tools for incentivizing data sharing across organizations. As I noted, the main motivation for...
55 days ago
The Practical Quant wrote a new blog post titled How social science research can inform the design of AI systems
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.In this episode of the Data Show, I spoke with Jacob Ward, a Berggruen Fellow at Stanford University. Ward has an extensive background in journalism, mainly covering topics in science and technology, at National Geographic, Al Jazeera, Discovery Channel, BBC, Popular Science, and many other outlets. Most recently, he’s become interested in the interplay between research in psychology, decision-making, and AI systems. He’s in the...
68 days ago
The Practical Quant wrote a new blog post titled Why it's hard to design fair machine learning models
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification,...
83 days ago
The Practical Quant wrote a new blog post titled Using machine learning to improve dialog flow in conversational applications
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and developers continue to refine and define tools for building conversational applications. We spoke...
96 days ago
The Practical Quant wrote a new blog post titled Building accessible tools for large-scale computation and machine learning
[A version of this post appears on the O'Reilly Radar.]In this episode of the Data Show, I spoke with Eric Jonas, a postdoc in the new Berkeley Center for Computational Imaging. Jonas is also affiliated with UC Berkeley’s RISE Lab. It was at a RISE Lab event that he first announced Pywren, a framework that lets data enthusiasts proficient with Python run existing code at massive scale on Amazon Web Services. Jonas and his collaborators are working on a related project, NumPyWren, a system for linear algebra built on a serverless architecture. Their hope is that by lowering the barrier to...
111 days ago
The Practical Quant wrote a new blog post titled Simplifying machine learning lifecycle management
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Harish Doddi on accelerating the path from prototype to production.In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries,...
125 days ago
The Practical Quant wrote a new blog post titled Notes from the first Ray meetup
[A version of this post appears on the O'Reilly Radar.]Ray is beginning to be used to power large-scale, real-time AI applications.Machine learning adoption is accelerating due to the growing number of large labeled data sets, languages aimed at data scientists (R, Julia, Python), frameworks (scikit-learn, PyTorch, TensorFlow, etc.), and tools for building infrastructure to support end-to-end applications. While some interesting applications of unsupervised learning are beginning to emerge, many current machine learning applications rely on supervised learning. In a recent series of posts,...
126 days ago
The Practical Quant wrote a new blog post titled How privacy-preserving techniques can lead to more robust machine learning models
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning.In this episode of the Data Show, I spoke with Chang Liu, applied research scientist at Georgian Partners. In a previous post, I highlighted early tools for privacy-preserving analytics, both for improving decision-making (business intelligence and analytics) and for enabling automation (machine learning). One of the tools I mentioned is an open source project for SQL-based analysis that adheres to...
139 days ago
The Practical Quant wrote a new blog post titled Specialized hardware for deep learning will unleash innovation
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Andrew Feldman on why deep learning is ushering a golden age for compute architecture.In this episode of the Data Show, I spoke with Andrew Feldman, founder and CEO of Cerebras Systems, a startup in the blossoming area of specialized hardware for machine learning. Since the release of AlexNet in 2012, we have seen an explosion in activity in machine learning, particularly in deep learning. A lot of the work to date happened primarily on general purpose hardware (CPU, GPU). But now that we’re six years into...
153 days ago
The Practical Quant wrote a new blog post titled Data collection and data markets in the age of privacy and machine learning
[A version of this post appears on the O'Reilly Radar.]While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data.In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the data community about the many interesting opportunities and challenges in data itself. Much of the focus of recent press coverage has been on algorithms and models, specifically the expanding utility of deep learning. Because large deep learning architectures are...
154 days ago
The Practical Quant wrote a new blog post titled What machine learning means for software development
[A version of this post appears on the O'Reilly Radar.]"Human in the loop" software development will be a big part of the future.Machine learning is poised to change the nature of software development in fundamental ways, perhaps for the first time since the invention of FORTRAN and LISP. It presents the first real challenge to our decades-old paradigms for programming. What will these changes mean for the millions of people who are now practicing software development? Will we see job losses and layoffs, or will see programming evolve into something different—perhaps even something more...
161 days ago
The Practical Quant wrote a new blog post titled Data regulations and privacy discussions are still in the early stages
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Aurélie Pols on GDPR, ethics, and ePrivacy.In this episode of the Data Show, I spoke with Aurélie Pols of Mind Your Privacy, one of my go-to resources when it comes to data privacy and data ethics. This interview took place at Strata Data London, a couple of days before the EU General Data Protection Regulation (GDPR) took effect. I wanted her perspective on this landmark regulation, as well as her take on trends in data privacy and growing interest in ethics among data professionals.Here are some...
167 days ago
The Practical Quant wrote a new blog post titled Managing risk in machine learning models
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Andrew Burt and Steven Touw on how companies can manage models they cannot fully explain.In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer at Immuta, and Steven Touw, co-founder and CTO of Immuta. Burt recently co-authored a white paper on managing risk in machine learning models, and I wanted to sit down with them to discuss some of the proposals they put forward to organizations that are deploying machine learning.Some high-profile examples of models gone awry have raised...
167 days ago
The Practical Quant wrote a new blog post titled Understanding automation
[A version of this post appears on the O'Reilly Radar.]An overview and framework, including tools that can be used to enable automation.In this post, I share slides and notes from a talk Roger Chen and I gavein May 2018 at the Artificial Intelligence Conference in New York. Most companies are beginning to explore how to use machine learning and AI, and we wanted to give an overview and framework for how to think about these technologies and their roles in automation. Along the way, we describe the machine learning and AI tools that can be used to enable automation.Let me begin by citing a...
167 days ago
The Practical Quant wrote a new blog post titled The real value of data requires a holistic view of the end-to-end data pipeline
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Ashok Srivastava on the emergence of machine learning and AI for enterprise applications.In this episode of the Data Show, I spoke with Ashok Srivastava, senior vice president and chief data officer at Intuit. He has a strong science and engineering background, combined with years of applying machine learning and data science in industry. Prior to joining Intuit, he led the teams responsible for data and artificial intelligence products at Verizon. I wanted his perspective on a range of issues, including...
167 days ago