Everything you need to know to get started with NumPy. The world runs on data and everyone should know how to work with it. It's hard to imagine a modern, tech-literate business that doesn't use data analysis, data science, machine learning, or artificial intelligence in some form. NumPy is at the core of all of those fields. While it's impossible to know exactly how many people are learning to analyze and work with data, it's a pretty safe assumption that tens of thousands (if not millions) of people need to understand NumPy and how to use it. Because of that, I've spent the last three months putting together what I hope is the best introductory guide to NumPy yet! If there's anything you want to see included in this tutorial, please leave a note in the comments or reach out any time! NumPy (Numerical Python) is an open-source Python library that's used in almost every field of science and engineering. NumPy users include everyone from beginning coders to experienced researchers doing state-of-the-art scientific and industrial research and development. The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages.
Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithms, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise. An alternative approach to data preparation is to grid search a suite of common and commonly useful data preparation techniques to the raw data. This is an alternative philosophy for data preparation that treats data transforms as another hyperparameter of the modeling pipeline to be searched and tuned.
Though often overlooked, cars serve as a rich data source. Millions of transportation vehicles whizz past us on a regular basis, each of which generate swaths of useful information that automakers are now figuring out how to monetize. Some of the biggest passenger car automakers have more than 10 million vehicles' worth of data sitting in their data repositories. Failure to tap into these vast data stores amounts to lost value-added for customers, lost safety opportunities and lost revenue and business intelligence. According to a McKinsey Report, "The overall revenue pool from car data monetization at a global scale might add up to USD 450 - 750 billion by 2030." In addition, according to a market analysis report on the Automotive Cyber Security Market, "The global automotive cyber security market size was valued at USD 1.44 billion in 2018 and is expected to grow at a compound annual growth rate (CAGR) of 21.4% from 2019 to 2025."
Artificial Intelligence (AI) is like a superhighway, it's moving fast, evolving, and growing quickly. Like most things in life, data scientists are not born with AI and Machine Learning (ML) knowledge. At H2O.ai, we are on a mission to democratize AI. To help every company become an AI company. Companies are also on an AI transformation journey.
Researchers at the US Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) have developed a novel machine learning approach to quickly enhance the resolution of wind velocity data by 50 times and solar irradiance data by 25 times--an enhancement that has never been achieved before with climate data. The researchers took an alternative approach by using adversarial training, in which the model produces physically realistic details by observing entire fields at a time, providing high-resolution climate data at a much faster rate. This approach will enable scientists to complete renewable energy studies in future climate scenarios faster and with more accuracy. "To be able to enhance the spatial and temporal resolution of climate forecasts hugely impacts not only energy planning, but agriculture, transportation, and so much more," said Ryan King, a senior computational scientist at NREL who specializes in physics-informed deep learning. Recommended AI News: Interlink Electronics Welcomes Aboard Edward Suski As Chief Product Officer King and NREL colleagues Karen Stengel, Andrew Glaws, and Dylan Hettinger authored a new article detailing their approach, titled "Adversarial super-resolution of climatological wind and solar data," which appears in the journal Proceedings of the National Academy of Sciences of the United States …
RealityEngines.AI, the machine learning startup co-founded by former AWS and Google exec Bindu Reddy, today announced that it is rebranding as Abacus.AI and launching its autonomous AI service into general availability. In addition, the company also today disclosed that it has raised a $13 million Series A round led by Index Ventures' Mike Volpi, who will also join the company's board. Seed investors Eric Schmidt, Jerry Yang and Ram Shriram also participated in this oversubscribed round, with Shriram also joining the company's board. This new round brings the company's total funding to $18.25 million. At its core, RealityEngines.AI's Abacus.AI's mission is to help businesses implement modern deep learning systems into their customer experience and business processes without having to do the heavy lifting of learning how to train models themselves.
In enterprise ML architectures, it's wise to maintain the outputs of the feature jobs in a sharable format without encoding. These features can be later cherrypicked, encoded, and fed into an ML model that needs it. This approach has several advantages. When features are readily available, the journey from a'business question' to'scientific answer' becomes much more simple. With the availability of feature pool, when a data scientist wants to do a new experiment, he/she does not have to start from the raw data. Instead he/she can start with the available features. This can avoid a lot of unoptimised runs. In the cases where they need more data-features, it can go as a request to the engineering team to optimally build whatever new is requested. And when they are confident to take the model to production environment, the model promotion will involve only minimal components.
Technology is the tool that is empowering the human race with its astonishing innovations. Almost every object that we come across is based on the foundation that has a significant share in digitizing our lives. Determining which is better data science or artificial intelligence is undermining one over another. However, both the technologies have their contribution to revolutionizing not only the business operations but also the lifestyle. This article aims to cover the technologies that have become a major sector of investment in the IT industry.
This is a Hands-on 1- hour Machine Learning Project using Pyspark. Pyspark is the collaboration of Apache Spark and Python. PySpark is a tool used in Big Data Analytics. Apache Spark is an open-source cluster-computing framework, built around speed, ease of use, and streaming analytics whereas Python is a general-purpose, high-level programming language. It provides a wide range of libraries and is majorly used for Machine Learning and Real-Time Streaming Analytics.
If you are currently in the market for almost any kind of enterprise software, you will almost certainly run across at least one vendor claiming that its product includes artificial intelligence (AI) capabilities. Of course, some of these claims are no more than marketing hyperbole, or "AI washing." However, in many cases, software makers truly are integrating new capabilities related to analytics, vision, natural language, or other areas that deserve the AI label. The market researchers at IDC have gone so far as to call AI "inescapable." Similarly, Omdia Tractica predicted that worldwide revenue from AI software will climb from $10.1 billion in 2018 to $126.0 billion in 2025, led in large part by advancements in deep learning technology.