There are two main tasks in speech processing. First one is to transform speech to text. The second is to convert the text into human speech. We will describe the general aspects of each API and then compare their main features in the table.
An introduction to the Initiative for Analytics and Data Science Standards (IADSS), who have launched a global research study aiming to gain insight about the analytics profession in the industry and help support the development of standards regarding analytics role definitions.
Optimizing a single objective, or a single point, is actually quite easy because there are no conflicting objectives. The real business challenge, and the source of much innovation, is trying to optimize a decision across multiple variables. Let’s explore this further.
BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks.
Key highlights from the Interspeech conference, with topics covering end-to-end models for automatic speech recognition, information theory approach to deep learning, speech processing and education, and more.
Also: Introduction to Statistics for Data Science; Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning; Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019
What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.
To prepare data for both analytics and machine learning initiatives teams can accelerate machine learning and data science projects to deliver an immersive business consumer experience that accelerates and automates the data-to-insight pipeline by following six critical steps.
We explain the key differences between explainability and interpretability and why they're so important for machine learning and AI, before taking a look at several techniques and methods for improving machine learning interpretability.
O'Reilly begins to shed some light on the accuracy/complexity tradeoff in machine learning, with An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI. Get the ebook now!
Before you figure out what skills you need to freshen up on, or the most optimal driving path to work to avoid traffic patterns, you need to make sure this new role is a right fit and that you'll be happy working there.
At Figure Eight, we're big believers in active learning. We think it holds the promise to better models, and that it's just about to go mainstream. In our new eBook, An Introduction to Active Learning, we cover the essentials. Download now!
Also: Learning Machine Learning vs Learning Data Science; Should you become a data scientist?; Learning Machine Learning vs Learning Data Science; Common mistakes when carrying out machine learning and data science; How Different are Conventional Programming and Machine Learning?
Whether MXNet is an entirely new framework for you or you have used the MXNet backend while training your Keras models, this tutorial illustrates how to build an image recognition model with an MXNet resnet_v1 model.
But it’s hard to avoid becoming a generalist if you don’t know which common problem classes you could specialize in in the fist place. That’s why I put together a list of the five problem classes that are often lumped together under the “data science” heading.
This ebook will walk you through four use cases for Machine Learning on Databricks, covering loan risk, advertising analytics and predictive use case, market basket analysis, suspicious behaviour identification in video use, and more.
AI, machine learning, and automated machine learning are transforming the healthcare industry and helping to solve some of today’s biggest healthcare challenges. In this DataRobot webinar, Dec 17, 1 PM EST, learn how AI technologies can help healthcare providers improve operational efficiency and patient experience.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.
When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.
Also: Data Science Projects Employers Want To See: How To Show A Business Impact; The Machine Learning Project Checklist; Here are the most popular Python IDEs / Editors; The Machine Learning Project Checklist
In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."
This free Ebook from DATAx offers advice on using AI and machine learning to enhance customer satisfaction, how chief data officers are taking the reins on AI strategy, successful case studies from across the business, and more.
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!
The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.
There are many techniques to detect and optionally remove outliers from a dataset. In this blog post, we show an implementation in KNIME Analytics Platform of four of the most frequently used - traditional and novel - techniques for outlier detection.
Written for programmers new to Python, this latest edition includes new exercises throughout. It covers features common to other languages concisely, while introducing Python's comprehensive standard functions library and unique features in detail.
Also: 7 Things You Need To Stop Doing To Be More Productive; #scikit-learn is used by 48% of @Kaggle champions, #Tensorflow by 16%, Keras by 14%; Linking Data Science Activities to Business Initiatives Using the Hypothesis Development Canvas
The best way to create better data science projects that employers want to see is to provide a business impact. This article highlights the process using customer churn prediction in R as a case-study.
It’s important to understand why we should do it so that we can be sure it’s a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.
Primary studies have always been a strength of marketing research. Many younger marketing researchers, however, have only been exposed to standardized ready-made research products or big data. This is a concern. What is the point of the word research in marketing research?
Coming soon: DataX New York, AI-2018 Cambridge UK, AI NEXTCon Seattle, Deep Learning Summit San Francisco, EGC France, H2O San Francisco, Business Of Bots Business of Bots San Francisco, TDWI Las Vegas, WSDM Melbourne, and more.
Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.
Move your career forward in one of the fields with the largest demand. Business Analytics at Clark University will give you the skills employers demand by teaching you how to synthesize data into powerful information.
Also: A Complete Guide to Choosing the Best Machine Learning Course; My secret sauce to be in top 2% of a Kaggle competition; Deep Learning for the Masses ( and The Semantic Layer); What is the Best Python IDE for Data Science?; 9 Must-have skills you need to become a Data Scientist, updated