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CUSTOMER KNOWLEDGE
The proven SVDS approach delivers the ability to understand what your customers really want and need. We help you build a unified, omnichannel view of customer behaviors and preferences, and develop models to explain and predict their actions. The result is a comprehensive understanding that enables more successful customer strategies and fuels AVOIDING COMMON MISTAKES WITH TIME SERIESSEE MORE ON SVDS.COM BIG DATA VISUALIZATION EXAMPLE: HISTORY OF ROCK MUSIC From that list we have added deeper context to the song with information about the artists, and analyses of qualitative attributes like energy level, and happy vs. sad. Trace the influence of bands beginning at the origin, Sister Rosetta Tharpe (1919 - 1973), to modern day groups such as Coldplay (1996 - present). TENSORFLOW RNN TUTORIAL For our RNN example, we use 9 time slices before and 9 after, for a total of 19 time points per window.With 26 cepstral coefficients, this is 494 data points per 25 ms observation. Depending on the data sampling rate, we recommend 26 cepstral features for THE VALUE OF EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to. GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
TOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniques AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSISSEE MORE ON SVDS.COM OPEN SOURCE TOOLKITS FOR SPEECH RECOGNITION Open Source Toolkits for Speech Recognition Looking at CMU Sphinx, Kaldi, HTK, Julius, and ISIP | February 23rd, 2017. As members of the deep learning R&D team at SVDS, we are interested in comparing Recurrent Neural Network (RNN) and other approaches to speech recognition. Until a few years ago, the state-of-the-art for speech recognition was a phonetic-based approach including ETHEREUM: RISE OF THE WORLD COMPUTER Ethereum: Rise of the World Computer February 23rd, 2016. The Ethereum network is a distributed economy like Bitcoin, except it is much, much more powerful.The essential difference is that Ethereum is programmable. In fact, it only takes a few minutes to program aCUSTOMER KNOWLEDGE
The proven SVDS approach delivers the ability to understand what your customers really want and need. We help you build a unified, omnichannel view of customer behaviors and preferences, and develop models to explain and predict their actions. The result is a comprehensive understanding that enables more successful customer strategies and fuels AVOIDING COMMON MISTAKES WITH TIME SERIESSEE MORE ON SVDS.COM BIG DATA VISUALIZATION EXAMPLE: HISTORY OF ROCK MUSIC From that list we have added deeper context to the song with information about the artists, and analyses of qualitative attributes like energy level, and happy vs. sad. Trace the influence of bands beginning at the origin, Sister Rosetta Tharpe (1919 - 1973), to modern day groups such as Coldplay (1996 - present). TENSORFLOW RNN TUTORIAL For our RNN example, we use 9 time slices before and 9 after, for a total of 19 time points per window.With 26 cepstral coefficients, this is 494 data points per 25 ms observation. Depending on the data sampling rate, we recommend 26 cepstral features for THE VALUE OF EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to. GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
TOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniques AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSISSEE MORE ON SVDS.COM OPEN SOURCE TOOLKITS FOR SPEECH RECOGNITION Open Source Toolkits for Speech Recognition Looking at CMU Sphinx, Kaldi, HTK, Julius, and ISIP | February 23rd, 2017. As members of the deep learning R&D team at SVDS, we are interested in comparing Recurrent Neural Network (RNN) and other approaches to speech recognition. Until a few years ago, the state-of-the-art for speech recognition was a phonetic-based approach including ETHEREUM: RISE OF THE WORLD COMPUTER Ethereum: Rise of the World Computer February 23rd, 2016. The Ethereum network is a distributed economy like Bitcoin, except it is much, much more powerful.The essential difference is that Ethereum is programmable. In fact, it only takes a few minutes to program a RESOURCES - SILICON VALLEY DATA SCIENCE This series published by O'Reilly Media will walk you through the complete process for creating a data strategy that will help your company solve real business challenges with data. Data Strategy Within Your Organization. From the Data Science Pop-up in Chicago in 2015, this one-hour fireside chat features VP of Data Strategy Scott Kurth.ALL BLOG POSTS
Merging Data Science and Business. Business leaders cannot afford to ignore their organization’s data—rather, that data should be used to make informed decisions. In this post, Principal Data Scientist Tom Fawcett and Professor of Data Science Foster Provost discuss how businesses can make the most of their analytical teams. THE BASICS OF CLASSIFIER EVALUATION: PART 2 1. With Scikit-learn, just invoke the proba() method of a classifier rather than classify().With Weka, click on More options and select the Output predictions box, or use -p on the command line.↩. 2. It’s common to perform some sort of Laplace correction to smooth out the estimates, to avoid getting extreme values when only a few examplesare present.
GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
THE VALUE OF EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to. THE BASICS OF CLASSIFIER EVALUATION: PART 1 The Basics of Classifier Evaluation: Part 1 August 5th, 2015 If it’s easy, it’s probably wrong. If you’re fresh out of a data science course, or have simply been trying to pick up the basics on your own, you’ve probably attacked a few data problems. THE ROI OF A MODERN DATA STRATEGY A modern data strategy will identify the optimal projects and corresponding implementation order so you can get the fastest ROI. In terms of measuring ROI, one of the best places to start is by looking at decreased costs or increased revenue that result from each project. Figure 1 provides a quick list of some basic examples before we diveinto
TOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniques MINDING YOUR DATA GAPS Minding Your Data Gaps Knowing which gaps to plug | April 13th, 2017. In an earlier post, we explained that to understand data gaps, you must start with your strategic business objectives (what you want to do with the data), understand the data being used, analyze the dimensions of data that are reflective of your needs, and look at how your current data fulfills these needs. BRAIN MONITORING WITH KAFKA, OPENTSDB, AND GRAFANA Brain Monitoring with Kafka, OpenTSDB, and Grafana July 14th, 2016. Here at SVDS, we’re a brainy bunch. So we were excited when Confluent announced their inaugural Kafka Hackathon.It was a great opportunity to take our passion for data science and engineering, and apply it to neuroscience. RESOURCES - SILICON VALLEY DATA SCIENCE The Data Value Chain. CTO John Akred explains the seven steps of the Data Value Chain — discover, ingest, process, persist, integrate, analyze, expose — and how you can put them to work. BIG DATA VISUALIZATION EXAMPLE: HISTORY OF ROCK MUSIC From that list we have added deeper context to the song with information about the artists, and analyses of qualitative attributes like energy level, and happy vs. sad. Trace the influence of bands beginning at the origin, Sister Rosetta Tharpe (1919 - 1973), to modern day groups such as Coldplay (1996 - present). GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
THE BASICS OF CLASSIFIER EVALUATION: PART 1SEE MORE ON SVDS.COM AVOIDING COMMON MISTAKES WITH TIME SERIESSEE MORE ON SVDS.COM TENSORFLOW RNN TUTORIAL TensorFlow RNN Tutorial Building, Training, and Improving on Existing Recurrent Neural Networks | March 23rd, 2017. On the deep learning R&D team at SVDS, we have investigated Recurrent Neural Networks (RNN) for exploring time series and developing speech recognition capabilities. OPEN SOURCE TOOLKITS FOR SPEECH RECOGNITION Open Source Toolkits for Speech Recognition Looking at CMU Sphinx, Kaldi, HTK, Julius, and ISIP | February 23rd, 2017. As members of the deep learning R&D team at SVDS, we are interested in comparing Recurrent Neural Network (RNN) and other approaches to speech recognition. Until a few years ago, the state-of-the-art for speech recognition was a phonetic-based approach including AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSISSEE MORE ON SVDS.COM ETHEREUM: RISE OF THE WORLD COMPUTER Ethereum: Rise of the World Computer February 23rd, 2016. The Ethereum network is a distributed economy like Bitcoin, except it is much, much more powerful.The essential difference is that Ethereum is programmable. In fact, it only takes a few minutes to program a WHY NOTEBOOKS ARE SUPER-CHARGING DATA SCIENCE Why are notebooks so exciting? There are three key areas that explain the continued popularity of notebooks. Collaboration. Previous solutions for collaboration in data science have been built on top of a closed world of tooling, constraining collaboration inside a particular platform. RESOURCES - SILICON VALLEY DATA SCIENCE The Data Value Chain. CTO John Akred explains the seven steps of the Data Value Chain — discover, ingest, process, persist, integrate, analyze, expose — and how you can put them to work. BIG DATA VISUALIZATION EXAMPLE: HISTORY OF ROCK MUSIC From that list we have added deeper context to the song with information about the artists, and analyses of qualitative attributes like energy level, and happy vs. sad. Trace the influence of bands beginning at the origin, Sister Rosetta Tharpe (1919 - 1973), to modern day groups such as Coldplay (1996 - present). GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
THE BASICS OF CLASSIFIER EVALUATION: PART 1SEE MORE ON SVDS.COM AVOIDING COMMON MISTAKES WITH TIME SERIESSEE MORE ON SVDS.COM TENSORFLOW RNN TUTORIAL TensorFlow RNN Tutorial Building, Training, and Improving on Existing Recurrent Neural Networks | March 23rd, 2017. On the deep learning R&D team at SVDS, we have investigated Recurrent Neural Networks (RNN) for exploring time series and developing speech recognition capabilities. OPEN SOURCE TOOLKITS FOR SPEECH RECOGNITION Open Source Toolkits for Speech Recognition Looking at CMU Sphinx, Kaldi, HTK, Julius, and ISIP | February 23rd, 2017. As members of the deep learning R&D team at SVDS, we are interested in comparing Recurrent Neural Network (RNN) and other approaches to speech recognition. Until a few years ago, the state-of-the-art for speech recognition was a phonetic-based approach including AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSISSEE MORE ON SVDS.COM ETHEREUM: RISE OF THE WORLD COMPUTER Ethereum: Rise of the World Computer February 23rd, 2016. The Ethereum network is a distributed economy like Bitcoin, except it is much, much more powerful.The essential difference is that Ethereum is programmable. In fact, it only takes a few minutes to program a WHY NOTEBOOKS ARE SUPER-CHARGING DATA SCIENCE Why are notebooks so exciting? There are three key areas that explain the continued popularity of notebooks. Collaboration. Previous solutions for collaboration in data science have been built on top of a closed world of tooling, constraining collaboration inside a particular platform. RESOURCES - SILICON VALLEY DATA SCIENCE The Data Value Chain. CTO John Akred explains the seven steps of the Data Value Chain — discover, ingest, process, persist, integrate, analyze, expose — and how you can put them to work.ALL BLOG POSTS
We thank our customers, partners, investors, friends and family for their support over the years. And most importantly, we thank our employees for their hard work and dedication to THE VALUE OF EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to.TOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniques ETHEREUM: RISE OF THE WORLD COMPUTER Ethereum: Rise of the World Computer February 23rd, 2016. The Ethereum network is a distributed economy like Bitcoin, except it is much, much more powerful.The essential difference is that Ethereum is programmable. In fact, it only takes a few minutes to program a WHY NOTEBOOKS ARE SUPER-CHARGING DATA SCIENCE Why are notebooks so exciting? There are three key areas that explain the continued popularity of notebooks. Collaboration. Previous solutions for collaboration in data science have been built on top of a closed world of tooling, constraining collaboration inside a particular platform. HOW MATURE ARE YOUR DATA CAPABILITIES? How Mature Are Your Data Capabilities? When technology isn't enough | April 27th, 2017. In a previous post on data maturity, we discussed a company that was just embarking on a transformation: launching a new services business and building data capabilities to support that business. But what if you’re not starting from the beginning? WHEN FAIR ISN'T PREDICTABLE: THE LAW OF AVERAGES When Fair Isn’t Predictable: The Law of Averages August 13th, 2013. When making decisions with data, the idea that things will “even out” may ring true, but it’s not always helpful. DEEPDREAM: ACCELERATING DEEP LEARNING WITH HARDWARE DeepDream: Accelerating Deep Learning With Hardware March 29th, 2017. Editor’s note: This was originally posted on Medium by Matthew Rubashkin. The exciting DeepGramAI Hackathon just concluded, and I wanted to share some of the cool things John Henning and myself built this weekend! Other cool projects at the Hackathon ranged from a speech to math decoder, to a semantic parser for KAFKA SIMPLE CONSUMER FAILURE RECOVERY Kafka Simple Consumer Failure Recovery June 21st, 2016. A modern data platform requires a robust Complex Event Processing (CEP) system, a cornerstone of which isCUSTOMER KNOWLEDGE
The proven SVDS approach delivers the ability to understand what your customers really want and need. We help you build a unified, omnichannel view of customer behaviors and preferences, and develop models to explain and predict their actions. The result is a comprehensive understanding that enables more successful customer strategies and fuelsALL BLOG POSTS
Merging Data Science and Business. Business leaders cannot afford to ignore their organization’s data—rather, that data should be used to make informed decisions. In this post, Principal Data Scientist Tom Fawcett and Professor of Data Science Foster Provost discuss how businesses can make the most of their analytical teams. BIG DATA VISUALIZATION EXAMPLE: HISTORY OF ROCK MUSIC From that list we have added deeper context to the song with information about the artists, and analyses of qualitative attributes like energy level, and happy vs. sad. Trace the influence of bands beginning at the origin, Sister Rosetta Tharpe (1919 - 1973), to modern day groups such as Coldplay (1996 - present). TENSORFLOW RNN TUTORIAL For our RNN example, we use 9 time slices before and 9 after, for a total of 19 time points per window.With 26 cepstral coefficients, this is 494 data points per 25 ms observation. Depending on the data sampling rate, we recommend 26 cepstral features for AVOIDING COMMON MISTAKES WITH TIME SERIESSEE MORE ON SVDS.COM THE VALUE OF EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to. AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSISSEE MORE ON SVDS.COMTOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniques HOW TO CHOOSE A DATA FORMAT ETHEREUM: RISE OF THE WORLD COMPUTER Ethereum: Rise of the World Computer February 23rd, 2016. The Ethereum network is a distributed economy like Bitcoin, except it is much, much more powerful.The essential difference is that Ethereum is programmable. In fact, it only takes a few minutes to program aCUSTOMER KNOWLEDGE
The proven SVDS approach delivers the ability to understand what your customers really want and need. We help you build a unified, omnichannel view of customer behaviors and preferences, and develop models to explain and predict their actions. The result is a comprehensive understanding that enables more successful customer strategies and fuelsALL BLOG POSTS
Merging Data Science and Business. Business leaders cannot afford to ignore their organization’s data—rather, that data should be used to make informed decisions. In this post, Principal Data Scientist Tom Fawcett and Professor of Data Science Foster Provost discuss how businesses can make the most of their analytical teams. BIG DATA VISUALIZATION EXAMPLE: HISTORY OF ROCK MUSIC From that list we have added deeper context to the song with information about the artists, and analyses of qualitative attributes like energy level, and happy vs. sad. Trace the influence of bands beginning at the origin, Sister Rosetta Tharpe (1919 - 1973), to modern day groups such as Coldplay (1996 - present). TENSORFLOW RNN TUTORIAL For our RNN example, we use 9 time slices before and 9 after, for a total of 19 time points per window.With 26 cepstral coefficients, this is 494 data points per 25 ms observation. Depending on the data sampling rate, we recommend 26 cepstral features for AVOIDING COMMON MISTAKES WITH TIME SERIESSEE MORE ON SVDS.COM THE VALUE OF EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to. AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSISSEE MORE ON SVDS.COMTOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniques HOW TO CHOOSE A DATA FORMAT ETHEREUM: RISE OF THE WORLD COMPUTER Ethereum: Rise of the World Computer February 23rd, 2016. The Ethereum network is a distributed economy like Bitcoin, except it is much, much more powerful.The essential difference is that Ethereum is programmable. In fact, it only takes a few minutes to program a RESOURCES - SILICON VALLEY DATA SCIENCE This series published by O'Reilly Media will walk you through the complete process for creating a data strategy that will help your company solve real business challenges with data. Data Strategy Within Your Organization. From the Data Science Pop-up in Chicago in 2015, this one-hour fireside chat features VP of Data Strategy Scott Kurth.ALL BLOG POSTS
Merging Data Science and Business. Business leaders cannot afford to ignore their organization’s data—rather, that data should be used to make informed decisions. In this post, Principal Data Scientist Tom Fawcett and Professor of Data Science Foster Provost discuss how businesses can make the most of their analytical teams. THE BASICS OF CLASSIFIER EVALUATION: PART 2 1. With Scikit-learn, just invoke the proba() method of a classifier rather than classify().With Weka, click on More options and select the Output predictions box, or use -p on the command line.↩. 2. It’s common to perform some sort of Laplace correction to smooth out the estimates, to avoid getting extreme values when only a few examplesare present.
GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
THE VALUE OF EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to. AVOIDING COMMON MISTAKES WITH TIME SERIES Avoiding Common Mistakes with Time Series January 28th, 2015. A basic mantra in statistics and data science is correlation is not causation, meaning that just because two things appear to be related to each other doesn’t mean that one causes the other.This is a lesson worth learning. If you work with data, throughout your career you’ll probably have to re-learn it several times. THE BASICS OF CLASSIFIER EVALUATION: PART 1 The Basics of Classifier Evaluation: Part 1 August 5th, 2015 If it’s easy, it’s probably wrong. If you’re fresh out of a data science course, or have simply been trying to pick up the basics on your own, you’ve probably attacked a few data problems. THE ROI OF A MODERN DATA STRATEGY A modern data strategy will identify the optimal projects and corresponding implementation order so you can get the fastest ROI. In terms of measuring ROI, one of the best places to start is by looking at decreased costs or increased revenue that result from each project. Figure 1 provides a quick list of some basic examples before we diveinto
LEARNING FROM IMBALANCED CLASSES Learning from imbalanced data has been studied actively for about two decades in machine learning. It’s been the subject of many papers, workshops, special sessions, and dissertations ( a recent survey has about 220 references). A vast number of techniques have been tried, with varying results and few clear answers.TOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniquesALL BLOG POSTS
Merging Data Science and Business. Business leaders cannot afford to ignore their organization’s data—rather, that data should be used to make informed decisions. In this post, Principal Data Scientist Tom Fawcett and Professor of Data Science Foster Provost discuss how businesses can make the most of their analytical teams. TENSORFLOW RNN TUTORIAL For our RNN example, we use 9 time slices before and 9 after, for a total of 19 time points per window.With 26 cepstral coefficients, this is 494 data points per 25 ms observation. Depending on the data sampling rate, we recommend 26 cepstral features forCUSTOMER KNOWLEDGE
The proven SVDS approach delivers the ability to understand what your customers really want and need. We help you build a unified, omnichannel view of customer behaviors and preferences, and develop models to explain and predict their actions. The result is a comprehensive understanding that enables more successful customer strategies and fuels BIG DATA VISUALIZATION EXAMPLE: HISTORY OF ROCK MUSICBIT OF DATA VIZDATA VIZ CHARTSDATA VIZ GALLERYAGE OF ROCKS DIAGRAMROCK AND ROLLMUSIC
From that list we have added deeper context to the song with information about the artists, and analyses of qualitative attributes like energy level, and happy vs. sad. Trace the influence of bands beginning at the origin, Sister Rosetta Tharpe (1919 - 1973), to modern day groups such as Coldplay (1996 - present). AVOIDING COMMON MISTAKES WITH TIME SERIESSEE MORE ON SVDS.COM THE BASICS OF CLASSIFIER EVALUATION: PART 1SEE MORE ON SVDS.COM THE VALUE OF EXPLORATORY DATA ANALYSISEXPLORATORY DATA ANALYSIS DEFINITIONEXPLORATORY DATA ANALYSIS EXAMPLEEXPLORATORY DATA ANALYSIS METHODSEXPLORATORY DATA ANALYSIS PROCESSEXPLORATORY DATA ANALYSIS REPORTWHAT IS EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to. GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
TOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniques AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSISSEE MORE ON SVDS.COMWHAT IS TIME SERIES ANALYSISTIME SERIES ANALYSIS EXAMPLETIME SERIES ANALYSIS EXCELTIME SERIES ANALYSIS PPTTIME SERIES ANALYSIS SOFTWARESTATISTICS FOR TIME SERIES DATAALL BLOG POSTS
Merging Data Science and Business. Business leaders cannot afford to ignore their organization’s data—rather, that data should be used to make informed decisions. In this post, Principal Data Scientist Tom Fawcett and Professor of Data Science Foster Provost discuss how businesses can make the most of their analytical teams. TENSORFLOW RNN TUTORIAL For our RNN example, we use 9 time slices before and 9 after, for a total of 19 time points per window.With 26 cepstral coefficients, this is 494 data points per 25 ms observation. Depending on the data sampling rate, we recommend 26 cepstral features forCUSTOMER KNOWLEDGE
The proven SVDS approach delivers the ability to understand what your customers really want and need. We help you build a unified, omnichannel view of customer behaviors and preferences, and develop models to explain and predict their actions. The result is a comprehensive understanding that enables more successful customer strategies and fuels BIG DATA VISUALIZATION EXAMPLE: HISTORY OF ROCK MUSICBIT OF DATA VIZDATA VIZ CHARTSDATA VIZ GALLERYAGE OF ROCKS DIAGRAMROCK AND ROLLMUSIC
From that list we have added deeper context to the song with information about the artists, and analyses of qualitative attributes like energy level, and happy vs. sad. Trace the influence of bands beginning at the origin, Sister Rosetta Tharpe (1919 - 1973), to modern day groups such as Coldplay (1996 - present). AVOIDING COMMON MISTAKES WITH TIME SERIESSEE MORE ON SVDS.COM THE BASICS OF CLASSIFIER EVALUATION: PART 1SEE MORE ON SVDS.COM THE VALUE OF EXPLORATORY DATA ANALYSISEXPLORATORY DATA ANALYSIS DEFINITIONEXPLORATORY DATA ANALYSIS EXAMPLEEXPLORATORY DATA ANALYSIS METHODSEXPLORATORY DATA ANALYSIS PROCESSEXPLORATORY DATA ANALYSIS REPORTWHAT IS EXPLORATORY DATA ANALYSIS Through these methods, the data scientist validates assumptions and identifies patterns that will inform the understanding of the problem and model selection, builds an intuition for the data to ensure high quality analysis, and validates that the data has been generated in the way it was expected to. GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
TOM FAWCETT
Tom Fawcett Tom has over 20 years experience applying machine learning and data science across five different companies. He is co-author of the highly regarded and top-selling book Data Science for Business (O’Reilly, 2013), which is now used in over 140 universities around the world.. Prior to joining SVDS, as a senior architect at Proofpoint, Tom applied machine learning techniques AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSISSEE MORE ON SVDS.COMWHAT IS TIME SERIES ANALYSISTIME SERIES ANALYSIS EXAMPLETIME SERIES ANALYSIS EXCELTIME SERIES ANALYSIS PPTTIME SERIES ANALYSIS SOFTWARESTATISTICS FOR TIME SERIES DATA RESOURCES - SILICON VALLEY DATA SCIENCE This series published by O'Reilly Media will walk you through the complete process for creating a data strategy that will help your company solve real business challenges with data. Data Strategy Within Your Organization. From the Data Science Pop-up in Chicago in 2015, this one-hour fireside chat features VP of Data Strategy Scott Kurth.CUSTOMER KNOWLEDGE
The proven SVDS approach delivers the ability to understand what your customers really want and need. We help you build a unified, omnichannel view of customer behaviors and preferences, and develop models to explain and predict their actions. The result is a comprehensive understanding that enables more successful customer strategies and fuels THE BASICS OF CLASSIFIER EVALUATION: PART 1 The Basics of Classifier Evaluation: Part 1 August 5th, 2015 If it’s easy, it’s probably wrong. If you’re fresh out of a data science course, or have simply been trying to pick up the basics on your own, you’ve probably attacked a few data problems. THE BASICS OF CLASSIFIER EVALUATION: PART 2 1. With Scikit-learn, just invoke the proba() method of a classifier rather than classify().With Weka, click on More options and select the Output predictions box, or use -p on the command line.↩. 2. It’s common to perform some sort of Laplace correction to smooth out the estimates, to avoid getting extreme values when only a few examplesare present.
GETTING STARTED WITH DEEP LEARNING Getting Started with Deep Learning A review of available tools | February 15th, 2017. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the modelperformance.
DATA INGESTION WITH SPARK AND KAFKA Now we can connect to the container and get familiar with some Kafka commands. Log into the container this way: 1. $ docker exec -it test_kafka /bin/bash. This is invoking the Docker client and telling it you wish to connect an interactive TTY to the container called test_kafka and start a bash shell. OPEN SOURCE TOOLKITS FOR SPEECH RECOGNITION Open Source Toolkits for Speech Recognition Looking at CMU Sphinx, Kaldi, HTK, Julius, and ISIP | February 23rd, 2017. As members of the deep learning R&D team at SVDS, we are interested in comparing Recurrent Neural Network (RNN) and other approaches to speech recognition. Until a few years ago, the state-of-the-art for speech recognition was a phonetic-based approach including ETHEREUM: RISE OF THE WORLD COMPUTER Ethereum: Rise of the World Computer February 23rd, 2016. The Ethereum network is a distributed economy like Bitcoin, except it is much, much more powerful.The essential difference is that Ethereum is programmable. In fact, it only takes a few minutes to program a AVOIDING COMMON MISTAKES WITH TIME SERIES ANALYSIS Avoiding Common Mistakes with Time Series Analysis January 19th, 2017. Editor’s note: Welcome to Throwback Thursdays! Every third Thursday of the month, we feature a classic post from the earlier days of our company, gently updated as appropriate. UNDERSTANDING THE CHIEF DATA OFFICER, 2E CDO by about a decade—and Chief Information Security Officers (CISOs). What’s novel—and where a CDO is best situated to add value to an organization—is the opportunity to create new productsand serv‐
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We thank our customers, partners, investors, friends and family for their support over the years. And most importantly, we thank our employees for their hard work and dedication to building a greatcompany!
December 31, 2017
HAPPY HOLIDAYS FROM SVDS Happy holidays from SVDS! We wish you peace, prosperity, and happiness this season and in the year ahead.December 28, 2017
CROSSING THE DEVELOPMENT TO PRODUCTION DIVIDE In this post we’ll give an overview of obstacles we’ve faced (you may be able to relate) and talk about solutions to overcome theseobstacles.
* HEATHER NELSON
December 21, 2017
Q&A: ON BEING DATA-DRIVEN The best way to spread data-driven thinking through an organization is by proving that you can use data to solve a real business problem.* EDD WILDER-JAMES
December 7, 2017
MANAGING UNCERTAINTY Being data-driven is the best way to manage uncertainty—but achieving that is about far more than bringing a bunch of numbers to your latest meeting.* EDD WILDER-JAMES
November 30, 2017
ANALYZING SENTIMENT IN CALTRAIN TWEETS As a first step to using Twitter activity as one of the data sources for train prediction, we start with a simple question: How do Twitter users currently feel about Caltrain?* CINDI THOMPSON ,
* BEN EVERSON
November 21, 2017
LEARNING FROM IMBALANCED CLASSES For this month’s Throwback Thursday, a post that provides insight and concrete advice on how to tackle imbalanced data.* TOM FAWCETT
November 16, 2017
EXPLORING THE POSSIBILITIES OF ARTIFICIAL INTELLIGENCE In this interview, Paco Nathan discusses making life more livable, AIfears, and more.
* MEG BLANCHETTE
November 9, 2017
MERGING DATA SCIENCE AND BUSINESS Business leaders cannot afford to ignore their organization’s data—rather, that data should be used to make informed decisions. In this post, Principal Data Scientist Tom Fawcett and Professor of Data Science Foster Provost discuss how businesses can make the most of their analytical teams. Tom and Foster are the authors of Data Science for Business. What aspect* MEG BLANCHETTE ,
* TOM FAWCETT
November 2, 2017
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