Understanding future technology trends may never have been as important as it is today. Check out the prediction of the 10 technology trends in 2021 from Baidu Research.
Software developers and cyber security experts have long fought the good fight against vulnerabilities in code to defend against hackers. A new, subtle approach to maliciously targeting machine learning models has been a recent hot topic in research, but its statistical nature makes it difficult to find and patch these so-called adversarial attacks. Such threats in the real-world are becoming imminent as the adoption of machine learning spreads, and a systematic defense must be implemented.
The rise in machine learning project implementation is coming, as is the the number of failures, due to several implementation and maintenance challenges. The first step of closing this gap lies in understanding the reasons for the failure.
Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?
We attempt to break down a problem of hand written alphabet image recognition into a simple process rather than using heavy packages. This is an attempt to create the data and then build a model using Support Vector Machines for Classification.
As data becomes the new ‘Gold’ for businesses, data scientists are set to find their value in this gold. This write-up clearly defines the job requirements and company expectations that this phenomenally evolving role entails.
Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data.
Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to Craig Smith about his work In 2020 and what he sees on the horizon for AI.
With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.
Also: Data Engineering — the Cousin of Data Science, is Troublesome; Build a Data Science Portfolio that Stands Out Using These Platforms; K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines; Popular Machine Learning Interview Questions
A Data Scientist must be a jack of many, many trades. Especially when working in broader teams, understanding the roles of others, such as data engineering, can help you validate progress and be aware of potential pitfalls. So, how can you convince your analysts to realize the importance of expanding their toolkit? Examples from real life often provide great insight.
Kaggle’s survey of ‘State of Data Science and Machine Learning 2020’ covers a lot of diverse topics. In this post, we are going to look at the popularity of cloud computing platforms and products among the data science and ML professionals participated in the survey.
The need to deal with the challenges and other smaller nuances of deploying machine learning models has given rise to the relatively new concept of MLOps. – a set of best practices aimed at automating the ML lifecycle, bringing together the ML system development and ML system operations.
Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?
Join technology experts, partners and analysts in the industry to see what is taking off in AI, cloud computing and putting models into production for better outcomes and trusted results. Register today!
Learn how to use TensorFlow Variables, their differences from plain Tensor objects, and when they are preferred over these Tensor objects | Deep Learning with TensorFlow 2.x.
We will show that the decomposition of growth into S-shaped logistic components also known as Loglet analysis, is more accurate as it takes into account the evolution of multiple covid waves.
Making your big break into the data science profession means standing out to potential employers in a crowd of tough competition. An important way to showcase your skills and experience is through the presentation of a portfolio. Following these recommendations for developing your portfolio will help you network effectively and stay on top of an ever-changing field.
Agile is not reserved for software developers only -- that's a myth. While these effective strategies are not commonly used by data scientists today and some aspects of data science make Agile a bit tricky, the methodology offers plenty of benefits to data science projects that can increase the effectiveness of your process and bring more success to your outcomes.
Snowflake the cloud data platform, is partnering, integrating products, and pursuing a joint go-to-market with Saturn Cloud to help data science teams get 100x faster results. Read more about developments and how to get started here.
In the context of machine learning, some of the concepts of information theory are used to characterize or compare probability distributions. Read up on the underlying math to gain a solid understanding of relevant aspects of information theory.
K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.
SQL is a foundational skill for data analysts but its application is sometimes limited within the data pipeline. However, SQL can be successfully used for many pre-processing tasks, such as data cleaning and wrangling, as demonstrated here by example.
This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; Approaches for machine diagnosis; Machine diagnosis using machine learning
These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author.
So, what do you do for a living? Sometimes, the answer to that question can feel like, "everything!" Well, for the Data Scientist, an extreme sense of being a "jack of all trades" is common. In fact, four such trades can be defined that a top-quality Data Scientist will iterate through during any one project.
These are the lessons and best practices I learned in many years of experience in data blending, and the software that became my most important tool in my day-to-day work.
Today, cloud data storage accounts for 45% of all enterprise data and by Q2 2021, that number could grow to 53%. Now is the time to embrace cloud than now.
Also: DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created; 10 Underappreciated Python Packages for Machine Learning Practitioners; Six Tips on Building a Data Science Team at a Small Company
Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.
OpenAI has released two new transformer architectures that combine image and language tasks in an fun and almost magical way. Read more about them here.
Developing machine learning algorithms requires implementing countless libraries and integrating many supporting tools and software packages. All this magic must be written by you in yet another tool -- the IDE -- that is fundamental to all your code work and can drive your productivity. These top Python IDEs and code editors are among the best tools available for you to consider, and are reviewed with their noteworthy features.
These trends broadly cover the three themes of: Where will businesses adopt AI in 2021? How will AI become more accessible? How will AI capabilities evolve?
Embarking on a new career path can be daunting with many unknowns about how to get started and how to be successful. If you are aspiring to become a Data Scientist, then the answers to these common questions can help set you off on the right foot.
CatalyzeX is a free browser extension that finds code implementations for ML/AI papers anywhere on the internet (Google, Arxiv, Twitter, Scholar, and other sites).
If you are considering starting a career path in machine learning and data science, then there is a great deal to learn theoretically, along with gaining practical skills in applying a broad range of techniques. This comprehensive learning plan will guide you to start on this path, and it is all available for free.
Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.
Marketing data science - data science related to marketing - is now a significant part of marketing. Some of it directly competes with traditional marketing research and many marketing researchers may wonder what the future holds in store for it.
Data engineering skills are currently in high demand. If you are looking for career prospects in this fast-growing profession, then these 10 skills and key factors will help you prepare to land an entry-level position in this field.
This post covers how StreamSets can help expedite operations at some of the most crucial stages of Machine Learning Lifecycle and MLOps, and demonstrates integration with Databricks and MLflow.
Also: SQL vs NoSQL: 7 Key Takeaways; Generating Beautiful Neural Network Visualizations; Meet whale! The stupidly simple data discovery tool; Key Data Science Algorithms Explained: From k-means to k-medoids clustering
Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.
When a company decides that they want to start leveraging their data for the first time, it can be a daunting task. Many businesses aren’t fully aware of all that goes into building a data science department. If you're the data scientist hired to make this happen, we have some tips to help you face the task head-on.
How to Get a Job as a Data Scientist
Want to Be a Data Scientist? Don’t Start With Machine Learning
Best Python IDEs and Code Editors You Should Know
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