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AWS introduced preview releases of Amazon HealthLake service and a feature for Amazon Redshift called Redshift ML during re:Invent 2020 in December. Amazon HealthLake is a data lake service that helps healthcare, health insurance, and pharmaceutical companies to derive value out of their data with the help of NLP. Redshift ML is a service that prov
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Photo by C M on Unsplash If you’re anything like me, you spent the first several months looking at applications of machine learning and wondering how to get better performance out of the model. I would spend hours, if not days, making minor tweaks to the model, hoping for better performance. Surely, I thought, there should be a better way to improv ...
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Latest picks: Squeezing LIME in a custom network by Thomas G. Data Science for Sustainability by Julia Nikulski Monte Carlo Integration in Python over Univariate and Multivariate Functions by Boyang Zhao Mapping Black-Owned Businesses with GeoPy and Folium by Avonlea Fisher In case you missed them: Machine Learning a Systems Engineering Perspective ...
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Source: Unsplash Azure Machine Learning (ML) is an end-to-end tool for managing machine learning and deep learning pipelines. It can automate training across large pools of computing resources, and help you deploy models in production and manage inference for production applications. As you adopt Azure ML in your organization, it is a good idea to ...
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How do you actually complete a machine learning project and what are some tools that can help each step of the way? Photo by Tolga Ulkan on Unsplash Everyone and their mother is getting into machine learning (ML) in this day and age. It seems that every company that is collecting data is trying to figure out some way to use AI and ML to analyze the ...
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From binary classification textbook cases to a real world OCR application. Like humans, machine learning models sometimes make mistakes when predicting a value from an input data point. But also like humans, most models are able to provide information about the reliability of these predictions. When you say “I’m sure that…” or “Maybe it is…”, you a ...
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How to Predict Churn with PySpark How to predict churn and save your business Photo by bruce mars on Unsplash To ‘churn’ is to move or to cause to move, vigorously. You might say that your stomach churns when you’re nervous or that a stormy sea is churning. Less poetically, a churn is a machine for making butter by shaking milk or cream. But today ...
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Posted by Alexander Ku, Software Engineer and Peter Anderson, Research Scientist, Google Research A core challenge in machine learning (ML) is to build agents that can navigate complex human environments in response to spoken or written commands. While today’s agents, including robots, can often navigate complicated environments, they cannot yet un ...
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Real-World ML: Warehouse Recognition System One of the Projects I recently had the opportunity to work on as a Team Lead of Rapid AI Solutions Prototyping Group at ICL Services [1], was implementing a Warehouse System for Recognition of Stored Factory Parts. The Problem is easy to understand: Warehouse workers (especially newbies) often can’t recog ...
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AlexNet is an important milestone in the visual recognition tasks in terms of available hardware utilization and several architectural choices. After its publication in 2012 by Alex Krizhevsky et al.[1] the popularity of deep learning and, in specific, the popularity of CNNs grew drastically. Below you can see the architecture of AlexNet: AlexNet A ...
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Let's talk about datasets for machine learning that change over time.
In real-life projects, datasets are rarely static. They grow, change, and evolve over time. But this fact is not reflected in how most datasets are maintained. Taking inspiration from software dev, where codebases are managed using Git, we can create living Git repositories fo ...
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Introduction Teaching basic data science, machine learning, and statistics is great due to the questions. Students ask brilliant questions, as they see what holes are present in your presentation and scaffolding. The students are not yet conditioned to ask only what you feel is easy to answer or present. They […] The post What is a Good Test Set Si
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Recommender systems may be the most common type of predictive model that the average person may encounter. They provide the basis for recommendations on services such as Amazon, Spotify, and Youtube. Recommender systems are a huge daunting topic if you’re just getting started. There is a myriad of data preparation techniques, algorithms, and model
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Stick to a commitment — and FINISH it. “Am I on the right path? Am I really learning?” Things might only start making sense later. “Am I on the right path? Am I really learning?” This was a question that I typically would ask myself in the middle of researching content or in the middle of building a project. Remember this word. Middle. 2 months ago ...
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A real-world Machine Learning project detailed step by step. Source: ActiveState This project is part of the online course Machine Learning, Data Science and Deep Learning by Sundog Education. The objective is to develop a classification algorithm that can predict whether a mammograph mass is benign or malignant with the highest accuracy possible. ...
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Under now-standard techniques, such as over-parameterization, batch-normalization, and adding residual links, “modern age” neural network training—at least for image classification tasks and many others—is usually quite stable. Using standard neural network architectures and training algorithms (typically SGD with momentum), the learned models perf
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Mining StockTwits Retail Sentiments for Momentum Trading TLDR: Using python to perform Natural Language Processing (NLP) on Tesla & Apple retail traders’ tweets mined from StockTwits, and use the sentiments as long / short signals for a trading algorithm. Performance Dashboard on Heroku: Link | Github Repo Image by Mayofi on Unsplash During a year ...
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