February, 2023

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The Best kept Secret in Data Science is KNIME

Mlearning.ai

Discover KNIME, the best kept secret in data science. This powerful and versatile open source platform offers a visual interface and wide… Continue reading on MLearning.

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A Day in the Life of a Senior Data Scientist

Matt Przybyla

Including a rundown of a common step-by-step project outline Continue reading on Towards Data Science ยป

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Google Cloud Unveils Its 2023 Data and AI Trends Report

insideBIGDATA

Google Cloud worked with IDC on multiple studies involving global organizations across industries in order to explore how data leaders are successfully addressing key data and AI challenges. The company compiled the results in its 2023 Data and AI Trends report. In it, you'll find the metrics-rich research behind the top five data and AI trends, along with tips and customer examples for incorporating them into your plans.

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Docker for Data Science Cheat Sheet

KDnuggets

Docker is dependency management on steroids, helping to ensure both reproducibility and collaboration, making it an important tool for data science. Our latest cheat sheet serves as a handy Docker reference. Check it out now!

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineersโ€™ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Training a PyTorch Model with DataLoader and Dataset

Machine Learning Mastery

When you build and train a PyTorch deep learning model, you can provide the training data in several different ways. Ultimately, a PyTorch model works like a function that takes a PyTorch tensor and returns you another tensor. You have a lot of freedom in how to get the input tensors. Probably the easiest is […] The post Training a PyTorch Model with DataLoader and Dataset appeared first on MachineLearningMastery.com.

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Announcing Ray support on Databricks and Apache Spark Clusters

databricks

Ray is a prominent compute framework for running scalable AI and Python workloads, offering a variety of distributed machine learning tools, large-scale hyperparameter.

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Interview โ€“ Business Intelligence und Process Mining ohne Vendor Lock-in!

Data Science Blog

Das Format Business Talk am Kudamm in Berlin fรผhrte ein Interview mit Benjamin Aunkofer zum Thema โ€œBusiness Intelligence und Process Mining nachhaltig umsetzenโ€. In dem Interview erklรคrt Benjamin Aunkofer , was gute Business Intelligence und Process Mining ausmacht und warum Unternehmen in jedem Fall daran arbeiten sollten, den gefรผrchteten Vendor Lock-In zu vermeiden, der gerade insbesondere bei Process Mining droht, jedoch leicht vermeidbar ist.

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Book Review: Tree-based Methods for Statistical Learning in R

insideBIGDATA

Here’s a new title that is a “must have” for any data scientist who uses the R language. It’s a wonderful learning resource for tree-based techniques in statistical learning, one that’s become my go-to text when I find the need to do a deep dive into various ML topic areas for my work.

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Learning Python in Four Weeks: A Roadmap

KDnuggets

Here is a roadmap for learning Python in four weeks, a combination of curated resources and ChatGPT prompts to master the language.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

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. Itโ€™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.

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Using Activation Functions in Deep Learning Models

Machine Learning Mastery

A deep learning model in its simplest form are layers of perceptrons connected in tandem. Without any activation functions, they are just matrix multiplications with limited power, regardless how many of them. Activation is the magic why neural network can be an approximation to a wide variety of non-linear function. In PyTorch, there are many […] The post Using Activation Functions in Deep Learning Models appeared first on MachineLearningMastery.com.

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Azure Databricks: A Comprehensive Guide

Analytics Vidhya

Introduction Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform that is built on top of the Microsoft Azure cloud. A collaborative and interactive workspace allows users to perform big data processing and machine learning tasks easily. In this blog post, we will take a closer look at Azure Databricks, its key features, […] The post Azure Databricks: A Comprehensive Guide appeared first on Analytics Vidhya.

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The Spectrum Of IT Partnerships

Adrian Bridgwater for Forbes

Technology vendors build software applications, suites, tools & platforms to clinch sales deals with customers who pay them for their products, services and ongoing support and maintenance. The vendor is the seller and the purchasing organization is the customer. Itโ€™s that simple. Except not always.

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Hereโ€™s why your efforts to extract value from data are going nowhere

Cassie Kozyrkov

The industry-wide neglect of data design and data quality (and what you can do about it) Continue reading on Towards Data Science ยป

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How to Build an Experimentation Culture for Data-Driven Product Development

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating

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Anomaly Detection: Its Real-Life Uses and the Latest Advances

insideBIGDATA

In this contributed article, Al Gharakhanian, Machine Learning Development Director, Cognityze, takes a look at anomaly detection in terms of real-life use cases, addressing critical factors, along with the relationship with machine learning and artificial neural networks.

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Data Cleaning with Python Cheat Sheet

KDnuggets

An intuitive guide that will help you to prepare and preprocess your dataset before applying the machine learning model.

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Using Learning Rate Schedule in PyTorch Training

Machine Learning Mastery

Training a neural network or large deep learning model is a difficult optimization task. The classical algorithm to train neural networks is called stochastic gradient descent. It has been well established that you can achieve increased performance and faster training on some problems by using a learning rate that changes during training. In this post, […] The post Using Learning Rate Schedule in PyTorch Training appeared first on MachineLearningMastery.com.

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Future of AI and Machine Learning in Cybersecurity

Analytics Vidhya

Introduction Artificial Intelligence (AI) and Machine Learning (ML) have rapidly become some of the most important technologies in the field of cybersecurity. With the increasing amount of data and sophisticated cyber threats, AI and ML are being used to strengthen the security of organizations and individuals. AI and ML are used to analyze large amounts of […] The post Future of AI and Machine Learning in Cybersecurity appeared first on Analytics Vidhya.

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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure youโ€™ve thought of everything to make your project a success! The list was created by Senzingโ€™s team of leading entity resolution experts, based on their real-world experience.

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Google unveils its experimental conversational AI service Bard

Dataconomy

The long-awaited debut of Google AI Bard finally happened. We previously shared with you that the tech giant is working on Google Apprentice Bard AI. Just days after the news, Google Code Red alarm seems to be paying off with a little name change.

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As Millions of Solar Panels Age Out, Recyclers Prepare to Cash In

Hacker News

Solar panels have a lifespan of 25 to 30 years, but they contain valuable metals, including silver and copper. With a surge of expired panels expected soon, companies are emerging that seek to recycle the reusable materials and keep the panels out of landfills.

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Video Highlights: Attention Is All You Need โ€“ Paper Explained

insideBIGDATA

In this video presentation, Mohammad Namvarpour presents a comprehensive study on Ashish Vaswani and his coauthors' renowned paper, โ€œAttention Is All You Need.โ€ This paper is a major turning point in deep learning research. The transformer architecture, which was introduced in this paper, is now used in a variety of state-of-the-art models in natural language processing and beyond.

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5 Statistical Paradoxes Data Scientists Should Know

KDnuggets

Knowing these 5 statistical paradoxes is essential for data scientists to improve their analyses and machine learning models.

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The Big Payoff of Application Analytics

Outdated or absent analytics wonโ€™t cut it in todayโ€™s data-driven applications โ€“ not for your end users, your development team, or your business. Thatโ€™s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.

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Using Dropout Regularization in PyTorch Models

Machine Learning Mastery

Dropout is a simple and powerful regularization technique for neural networks and deep learning models. In this post, you will discover the Dropout regularization technique and how to apply it to your models in PyTorch models. After reading this post, you will know: How the Dropout regularization technique works How to use Dropout on your […] The post Using Dropout Regularization in PyTorch Models appeared first on MachineLearningMastery.com.

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Learning the Basics of Deep learning, ChatGPT, and Bard AI

Analytics Vidhya

Introduction Artificial Intelligence is the ability of a computer to work or think like humans. So many Artificial Intelligence applications have been developed and are available for public use, and chatGPT is a recent one by Open AI. ChatGPT is an artificial intelligence model that uses the deep model to produce human-like text. It predicts […] The post Learning the Basics of Deep learning, ChatGPT, and Bard AI appeared first on Analytics Vidhya.

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Decisions made better: Comparing the role of AI and AU

Dataconomy

As the world becomes increasingly digital, businesses are turning to technology to stay ahead of the competition. Data-driven decision making is becoming more critical than ever before, and two technologies that have captured the imagination of businesses worldwide are artificial intelligence (AI) and augmented intelligence (AU).

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Amazon Removes Books From Kindle Unlimited After They Appear on Pirate Sites

Hacker News

When Amazon launched the first Kindle fifteen years ago, book piracy was already a common problem. When publishers clashed with The Pirate Bay over illegally shared copies, we envisioned that things could get much worse if Kindle-ready pirate sites began to pop up. Rempant Book Piracy Fast forward to today and book piracy is easier and more widespread than ever.

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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.