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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 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...
7 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...
23 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,...
37 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,...
38 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...
51 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...
65 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...
66 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...
73 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...
79 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...
79 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...
79 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...
79 days ago
The Practical Quant wrote a new blog post titled The evolution of data science, data engineering, and AI
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: A special episode to mark the 100th episode.This episode of the Data Showmarks our 100th episode. This podcast stemmed out of video interviews conducted at O’Reilly’s 2014 Foo Camp. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them. We originally conceived of using those initial conversations to be the basis of a regular series of video interviews. The logistics of studio interviews proved too...
121 days ago
The Practical Quant wrote a new blog post titled Companies in China are moving quickly to embrace AI technologies
[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Jason Dai on the first year of BigDL and AI in China.In this episode of the Data Show, I spoke with Jason Dai, CTO of Big Data Technologies at Intel, and one of my co-chairs for the AI Conference in Beijing. I wanted to check in on the status of BigDL, specifically how companies have been using this deep learning library on top of Apache Spark, and discuss some newly added features. It turns out there are quite a number of companies already using BigDL in production, and we talked about some of the popular...
135 days ago
The Practical Quant wrote a new blog post titled How to build analytic products in an age when data privacy has become critical
[A version of this post appears on the O'Reilly Radar.]Privacy-preserving analytics is not only possible, but with GDPR about to come online, it will become necessary to incorporate privacy in your data products.In this post, I share slides and notes from a talk I gave in March 2018 at the Strata Data Conference in California, offering suggestions for how companies may want to build analytic products in an age when data privacy has become critical. A lot has changed since I gave this presentation: numerous articles have been written about Facebook’s privacy policies, its CEO...
142 days ago
The Practical Quant wrote a new blog post titled Teaching and implementing data science and AI in the enterprise
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Jerry Overton on organizing data teams, agile experimentation, and the importance of ethics in data science.In this episode of the Data Show, I spoke with Jerry Overton, senior principal and distinguished technologist at DXC Technology. I wanted the perspective of someone who works across industries and with a variety of companies. I specifically wanted to explore the current state of data science and AI within companies and public sector agencies. As much as we talk about use cases, technologies, and...
149 days ago
The Practical Quant wrote a new blog post titled Building tools for the AI applications of tomorrow
[A version of this post appears on the O'Reilly Radar.]We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.By Ben Lorica and Mike LoukidesFor the last few years, AI has been almost synonymous with deep learning (DL). We’ve seen AlphaGo touted as an example of deep learning. We’ve seen deep learning used for naming paint colors (not very successfully), imitating Rembrandt and other great painters, and many other applications. Deep learning has been successful in part because, as François Chollet tweeted, “you can achieve a surprising amount...
149 days ago
The Practical Quant wrote a new blog post titled The importance of transparency and user control in machine learning
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations.In this episode of the Data Show, I spoke with Guillaume Chaslot, an ex-YouTube engineer and founder of AlgoTransparency, an organization dedicated to helping the public understand the profound impact algorithms have on our lives. We live in an age when many of our interactions with companies and services are governed by algorithms. At a time when their impact continues to grow, there are many settings where these algorithms are far from...
162 days ago
The Practical Quant wrote a new blog post titled What machine learning engineers need to know
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.In this episode of the Data Show, I spoke Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon. This conversation grew out of a recent email thread the three of us had on machine learning engineers, a new job role that LinkedIn recently pegged as the fastest growing job in the U.S. In our email discussion, there was some...
177 days ago
The Practical Quant wrote a new blog post titled How to train and deploy deep learning at scale
[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine learning (ML).We discussed using and deploying deep learning at scale. This is an empirical era for...
191 days ago