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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
The Ultimate Scikit-Learn Machine Learning Cheatsheet, by Andre Ye - Jan 25, 2021.
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.
Building a Deep Learning Based Reverse Image Search, by Vegard Flovik - Jan 22, 2021.
Following the journey from unstructured data to content based image retrieval.
Best Python IDEs and Code Editors You Should Know, by Claire D. Costa - Jan 8, 2021.
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.
The Ultimate Scikit-Learn Machine Learning Cheatsheet
Building a Deep Learning Based Reverse Image Search
Best Python IDEs and Code Editors You Should Know
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