Analytics Vidhya
JULY 21, 2019
Overview Mask R-CNN is a state-of-the-art framework for Image Segmentation tasks We will learn how Mask R-CNN works in a step-by-step manner We will. The post Computer Vision Tutorial: Implementing Mask R-CNN for Image Segmentation (with Python Code) appeared first on Analytics Vidhya.
KDnuggets
JULY 26, 2019
Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Dataconomy
JULY 30, 2019
A recent survey of over 225 enterprise Data Scientists, AI technologists and business stakeholders involved in active AI and machine learning (ML) projects, suggests that for most organizations, it’s still early days for AI technology. The AI market is projected to become a $190 billion industry by 2025 ( according. The post Why 96% of Enterprises Face AI Training Data Issues appeared first on Dataconomy.
Smart Data Collective
JULY 30, 2019
In an earlier age, perhaps as little as a decade ago, businesses had to rely on intuition and educated guesses to guide their spending. The situation was famously captured by John Wanamaker, who said, “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.” Today, data is everywhere. Phones track our locations and our social media usage.
Advertisement
Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?
DataRobot
JULY 10, 2019
As machine learning and artificial intelligence (AI) usher in the Fourth Industrial Revolution, it seems like everyone wants to get in on the action. And who can blame them? AI promises improved accuracy, speed, scalability, personalization, consistency, and clarity in every area of business. With all those benefits, why are some businesses hesitating to move forward?
DataRobot Blog
JULY 9, 2019
by Jen Underwood. To retain market leadership in the algorithm economy, enterprises require new ways to maximize the value of data and AI with citizen data scientists. Don’t think citizen data science is. Read More.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
KDnuggets
JULY 29, 2019
Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations.
Dataconomy
JULY 18, 2019
If you are a Data Scientist wondering what companies could have the most career opportunities or an employer looking to hire the best data science talent but aren’t sure what titles to use in your job listings — a recent report using Diffbot’s Knowledge Graph could hold some answers for. The post Three Trends in Data Science Jobs You Should Know appeared first on Dataconomy.
Smart Data Collective
JULY 9, 2019
Business success begins and ends with customer experience. According to recent studies, 90 percent of buyers would gladly spend more for a better customer experience. That’s exactly why understanding what an improved experience means for customers is so important. Companies have been able to perform more in-depth customer analysis—above and beyond social media commentary and feedback surveys—with the development and proliferation of analytics.
Data Science 101
JULY 8, 2019
Online courses are a great way to share knowledge with others; that is why I have decided to launch a few courses. The first course is Intro to Azure ML Studio – Regression. This is a smaller course and should take about 2 hours to complete. Azure ML Studio is a drag-and-drop interface for doing machine learning. Topics are all based upon Azure ML Studio, and they include: Linear Regression Linear Correlation Feature Selection Splitting Data Evaluating a model.
Advertisement
Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.
DataRobot
JULY 25, 2019
In our first blog post on the topic of AI Ethics, we covered the promise that artificial intelligence (AI) holds to improve the speed, accuracy, and operations of businesses across a range of industries. With the potential of AI, it’s hard to believe that businesses are hesitating to move forward with AI projects, but fear holds people back. They fear making mistakes that could damage their company’s reputation or doing something illegal or unethical.
Analytics Vidhya
JULY 10, 2019
Overview Presenting 21 open source tools for Machine Learning you might not have come across Each open-source tool here adds a different aspect to. The post 21 Must-Know Open Source Tools for Machine Learning you Probably Aren’t Using (but should!) appeared first on Analytics Vidhya.
KDnuggets
JULY 24, 2019
Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.
Dataconomy
JULY 9, 2019
Can you fight cancer with the help of Big Data and Machine Learning? How can these technologies help in the procedure of diagnosis, drug discovery and treating cancer? Cancer is an ailment with a long tail distribution. This implies there are different explanations for this condition to take place with. The post Have we lost the cancer battle? No! – say Big Data and Machine Learning appeared first on Dataconomy.
Speaker: Scott Sehlhorst
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
Smart Data Collective
JULY 12, 2019
Do you remember what it was like trying to buy a house before the days of online listings? If you were born after about 1980, you probably never experienced that. The internet has catapulted the real estate industry into a world where people can buy houses site-unseen and sign the paperwork without ever meeting in person. And it’s only just beginning. 2019 has hailed in an incredible era of mobile-centric technology, automation, smart tools, and other electronic marvels that make real estate tra
Data Science 101
JULY 31, 2019
A new online conference focused on cloud data technologies is coming this fall. It is not just a conference or webinar, it will be an interactive online platform. The focus of the event is data in the cloud (migrating, storing and machine learning). You can pre-register for the conference now. Some of the topics from the summit include: Data Science IoT Streaming Data AI Data Visualization.
DataRobot
JULY 1, 2019
“Women and people of color are fighting many battles in the tech world and in the fast-growing world of artificial intelligence.” ( The New York Times ) Because AI models are often a reflection of the humans who train and manage them, incidents of AI bias are still found in many industries and in organizations all over the world. When companies build diverse teams, they will usually have more diverse AI models that can help them overcome bias.
Analytics Vidhya
JULY 14, 2019
Overview Learn how to build your very own speech-to-text model using Python in this article The ability to weave deep learning skills with NLP. The post Learn how to Build your own Speech-to-Text Model (using Python) appeared first on Analytics Vidhya.
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
KDnuggets
JULY 29, 2019
At my workplace, we produce a lot of functional prototypes for our clients. Because of this, I often need to make Small Data go a long way. In this article, I’ll share 7 tips to improve your results when prototyping with small datasets.
Dataconomy
JULY 26, 2019
With a saturated analytics and business intelligence (A&BI) market, why are we still struggling to make analytics platforms work for Data Scientists? And perhaps more importantly, why are we failing to see a return on our expensive Data Science initiatives? It’s not for a lack of effort, a lack of. The post Why analytics platforms are failing your Data Scientists appeared first on Dataconomy.
Smart Data Collective
JULY 30, 2019
Computer breaches from Russian or Chinese hackers get the headlines, but the reality is you are more likely to be a victim from an insider. It turns out that as much as 60 percent of all attacks were carried out by insiders – either overtly or inadvertently. The High Cost of Breaches. If it’s your business that falls victim, the cost can be high. Your company’s reputation can be damaged.
Victor Zhou
JULY 2, 2019
Disclaimer: some links in this post are affiliate links. These help support the blog by allowing me to receive a bonus if you sign up. When I started this blog , I used Mailchimp to create an email newsletter so readers could sign up to get new posts in their inbox. This was an easy choice: Mailchimp has a good free tier, is super easy to setup, and is a pleasure to use.
Speaker: Kevin Kai Wong, President of Emergent Energy Solutions
In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.
DataRobot
JULY 15, 2019
“The unprecedented explosion in the amount of information we are generating and collecting, thanks to the arrival of the internet and the always-online society, powers all the incredible advances we see today in the field of artificial intelligence (AI) and Big Data.” ( Forbes ) Organizations around the world are leveraging this explosion of data to solve some of their biggest business problems.
Analytics Vidhya
JULY 3, 2019
Overview Presenting 11 data science videos that will enhance and expand your current skillset We have categorized these videos into three fields – Natural. The post 11 Superb Data Science Videos Every Data Scientist Must Watch appeared first on Analytics Vidhya.
KDnuggets
JULY 31, 2019
I had a feeling that R has developed as a language to such a degree that many of us are using it now in completely different ways. This means that there are likely to be numerous tricks, packages, functions, etc that each of us use, but that others are completely unaware of, and would find useful if they knew about them.
Dataconomy
JULY 3, 2019
What is the future of AI in Europe and what does it take to build an AI solution that is attractive to investors and customers at the same time? How do we reimagine the battle of “AI vs Human Creativity” in Europe? Is there any company that is not using. The post Where does Europe stand in the development of AI? appeared first on Dataconomy.
Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage
Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.
Smart Data Collective
JULY 19, 2019
When Alan Turing invented the first intelligent machine , few could have predicted that the advanced technology would become as widespread and ubiquitous as it is today. Since then, companies have adopted AI for pretty much everything , from self-driving cars to medical technology to banking. We live in the age of big data, an age in which we use machines to collect and analyze massive amounts of data in a way that humans couldn’t do on their own.
Victor Zhou
JULY 30, 2019
This 4-post series, written especially with beginners in mind, provides a fundamentals-oriented approach towards understanding Neural Networks. We’ll start with an introduction to classic Neural Networks for complete beginners before delving into two popular variants: Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). For each of each these types of networks, we’ll: See the structure of the network.
DataCentric podcast
JULY 10, 2019
It's been a big week for IBM. The company closed its acquisition of Red Hat, while also expanding its storage offerings. We bring in IBM's Storage group CMO Eric Herzog to tell us all about the new storage offerings. For Eric, it's all about Applications, Workloads, and Use Cases, but with a strong focus on data protection and Big Data/AI.
Analytics Vidhya
JULY 18, 2019
Overview We look at the latest state-of-the-art NLP library in this article called PyTorch-Transformers We will also implement PyTorch-Transformers in Python using popular NLP. The post Introduction to PyTorch-Transformers: An Incredible Library for State-of-the-Art NLP (with Python code) appeared first on Analytics Vidhya.
Advertisement
Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.
Expert insights. Personalized for you.
307 
Let's personalize your content