Sat.Oct 30, 2021 - Fri.Nov 05, 2021

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TensorFlow for Beginners With Examples and Python Implementation

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction You’ve probably heard of TensorFlow if you’re a machine learning student. It has become an industry norm and is one of the most common tools for machine learning and deep learning experts. TensorFlow is a free and open-source library for creating machine […].

Python 394
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Real-world challenges for AGI

DeepMind

When people picture a world with artificial general intelligence (AGI), robots are more likely to come to mind than enabling solutions to society’s most intractable problems. But I believe the latter is much closer to the truth. AI is already enabling huge leaps in tackling fundamental challenges: from solving protein folding to predicting accurate weather patterns, scientists are increasingly using AI to deduce the rules and principles that underpin highly complex real-world domains - ones they

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Introducing spaCy v3.2

Explosion

We’re pleased to present v3.2 of the spaCy Natural Language Processing library. Since v3.1 we’ve added usability improvements for custom training and scoring, improved performance on Apple M1 and Nvidia GPU hardware, and support for space-efficient vectors using floret , our new hash embedding extension to fastText. The spaCy team has gotten a lot bigger this year, and we’ve got lots of exciting features and examples coming up, including example projects for data augmentation and model distillat

NLP 52
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Avoiding Data Disasters

fast.ai

Things can go disastrously wrong in data science and machine learning projects when we undervalue data work, use data in contexts that it wasn’t gathered for, or ignore the crucial role that humans play in the data science pipeline. A new multi-university centre focused on Information Resilience , funded by the Australian government’s top scientific funding body (ARC), has recently launched.

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Usage-Based Monetization Musts: A Roadmap for Sustainable Revenue Growth

Speaker: David Warren and Kevin O’Neill Stoll

Transitioning to a usage-based business model offers powerful growth opportunities but comes with unique challenges. How do you validate strategies, reduce risks, and ensure alignment with customer value? Join us for a deep dive into designing effective pilots that test the waters and drive success in usage-based revenue. Discover how to develop a pilot that captures real customer feedback, aligns internal teams with usage metrics, and rethinks sales incentives to prioritize lasting customer eng

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Understanding Transfer Learning for Deep Learning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview What is Transfer Learning and it’s Working How Transfer Learning Works Why Should You Use Transfer Learning? When to use Transfer Learning Models That Have Been Pre-Trained The reuse of a previously learned model on a new problem is known as […]. The post Understanding Transfer Learning for Deep Learning appeared first on Analytics Vidhya.

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Zero-Click Buying: Is This The New Standard In eCommerce?

Dlabs.ai

When Amazon introduced ‘ 1-click ’ in 1997, retailers trembled. The innovation gave customers a way to purchase anything from the eCommerce store with just one click. And the feature soon set the standard for buying anything online. Fast forward to today, and expectations have shifted again. Consumers now think that retailers should know what they want, often before they realize it themselves.

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Stanford AI Lab Papers at CoRL 2021

The Stanford AI Lab Blog

The Conference on Robot Learning (CoRL 2021) will take place next week. We’re excited to share all the work from SAIL that will be presented, and you’ll find links to papers, videos and blogs below. Feel free to reach out to the contact authors directly to learn more about the work that’s happening at Stanford! List of Accepted Papers LILA: Language-Informed Latent Actions Authors : Siddharth Karamcheti*, Megha Srivastava*, Percy Liang, Dorsa Sadigh Contact : skaramcheti@cs.stanford.edu, megha@c

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Building an Infinite Timer using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview What is this “Infinite timer in Python”? What are its uses? How to make it? What is this “Infinite Timer using python”? Infinite timer using Python is a program written in Python using its libraries. It serves as a reminder by notifying […]. The post Building an Infinite Timer using Python appeared first on Analytics Vidhya.

Python 377
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Introducing spaCy v3.2

Explosion

spaCy v3.2 features usability improvements for custom training and scoring, improved performance and support for floret, our new fastText word vectors algorithm.

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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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An Introduction to Particle Swarm Optimization (PSO) Algorithm

Analytics Vidhya

Overview: PSO is a stochastic optimization technique based on the movement and intelligence of swarms. In PSO, the concept of social interaction is used for solving a problem. It uses a number of particles (agents) that constitute a swarm moving around in the search space, looking for the best solution. Each particle in the swarm […]. The post An Introduction to Particle Swarm Optimization (PSO) Algorithm appeared first on Analytics Vidhya.

Algorithm 360
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An Introduction to Automated Multi-Class Text Classification

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Overview In this article, we are going to discuss automated multi-class classification on the mixed data type. Think about text classification. When we have a bunch of text and a target label. Based on the incoming text we create a model to learn […]. The post An Introduction to Automated Multi-Class Text Classification appeared first on Analytics Vidhya.

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A Complete Guide to Dealing with Missing values in Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Hello there, guys! Good day, everyone! Today we’ll look at an intriguing issue in data pre-processing: how to deal with missing values (which is part of Data Cleaning). So, before we get into the meat of the matter, let’s review some fundamental terminology […]. The post A Complete Guide to Dealing with Missing values in Python appeared first on Analytics Vidhya.

Python 352
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Plotting Images Using Matplotlib Library in Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction to Matplotlib Matplotlib is a widely used data visualization library in python. This article illustrates how to display, modify and save an image using the ‘matplotlib’ library. We will see how to use the ‘image’ module as it makes working with images […]. The post Plotting Images Using Matplotlib Library in Python appeared first on Analytics Vidhya.

Python 359
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From Diagnosis to Delivery: How AI is Revolutionizing the Patient Experience

Speaker: Simran Kaur, Founder & CEO at Tattva Health Inc.

The healthcare landscape is being revolutionized by AI and cutting-edge digital technologies, reshaping how patients receive care and interact with providers. In this webinar led by Simran Kaur, we will explore how AI-driven solutions are enhancing patient communication, improving care quality, and empowering preventive and predictive medicine. You'll also learn how AI is streamlining healthcare processes, helping providers offer more efficient, personalized care and enabling faster, data-driven

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Training Neural Network with Keras and basics of Deep Learning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Overview Keras is a Python library including an API for working with neural networks and deep learning frameworks. Keras includes Python-based methods and components for working with various Deep Learning applications. source: keras.io Table of Contents What exactly is Keras? Models Explaining Deep […].

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Beginner’s Guide to AutoML with an Easy AutoGluon Example

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Machine Learning is popular and is being used everywhere for applications ranging from financial services to healthcare, marketing & advertising to manufacturing. Almost all industries seem to derive substantial benefit using some form of Machine Learning. Over the recent past, automation technology also […].

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A comprehensive guide for Camera calibration in computer vision

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview of Camera Calibration A camera is a device that converts the 3D world into a 2D image. A camera plays a very important role in capturing three-dimensional images and storing them in two-dimensional images. To know the mathematics behind it is extremely […]. The post A comprehensive guide for Camera calibration in computer vision appeared first on Analytics Vidhya.

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Facial Emotion Detection Using CNN

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. An End to End guide for model training and deployment for facial emotion detection using the webcam. In our previous article, we have explored emotion detection in the text, which is quite helpful for several use cases, you can read the article here. […]. The post Facial Emotion Detection Using CNN appeared first on Analytics Vidhya.

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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

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PCA(Principal Component Analysis) on MNIST Dataset

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction to PCA Hello Learners, Welcome! In this article, we are going to learn about PCA and its implementation on the MNIST dataset. the full form of PCA is Principal Component Analysis. in this article, we are going to implement the PCA technic […]. The post PCA(Principal Component Analysis) on MNIST Dataset appeared first on Analytics Vidhya.

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Visualize data using Parallel Coordinates Plot

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Overview of Parallel Coordinates Plot While using visualizations, one compact visualization showing the relation between multiple variables has an upper hand over multiple visualizations – one for each variable. When you are trying to visualize high dimensional numerical data instead of multiple bar/line […].

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Employee Attrition Prediction – A Comprehensive Guide

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction In this article, we will cover everything from gathering data to preparing the steps for model training and evaluation. Deep learning algorithms can have huge functional uses when provided with quality data to sort through. Diverse fields such as sales forecasting and […].

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Startups profit prediction using Multiple Linear Regression

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Overview In this article, we will be predicting the profit from the startup’s dataset with the features available to us. We’re using the 50-startups dataset for this problem statement and we will be using the concept of Multiple linear regression to predict the […].

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The Tumultuous IT Landscape Is Making Hiring More Difficult

After a year of sporadic hiring and uncertain investment areas, tech leaders are scrambling to figure out what’s next. This whitepaper reveals how tech leaders are hiring and investing for the future. Download today to learn more!

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Learn how to build your Discord bot using DailoGPT and HuggingFace API

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Design your own Discord app using Hugging face API and deploy it on Replit server. Learn tips and tricks about building discord bot server, uptimerobot, Kaggle API, and Replit. Image 1 Introduction to building your own Discord bot If you ever wonder how […]. The post Learn how to build your Discord bot using DailoGPT and HuggingFace API appeared first on Analytics Vidhya.

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Exploratory Data Analysis on UBER Stocks Dataset

Analytics Vidhya

This article was published as a part of the Data Science Blogathon What is EDA(Exploratory data analysis)? Exploratory data analysis is a great way of understanding and analyzing the data sets. The EDA technique is extensively used by data scientists and data analysts to summarize the main characteristics of data sets and to visualize them through […].

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Implementing Particle Swarm Optimization using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction There are multiple ways that one can take to either minimize or maximize any function so that the optimal value can be found out. You can find several optimisation solutions on the internet but in the end, no one solution is the […]. The post Implementing Particle Swarm Optimization using Python appeared first on Analytics Vidhya.

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An Intuition about Kalman Filter for Computer Vision

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview The importance of the Kalman filter relies on removing the noise from input data. It is one of the great tools to have during the data capturing process. It is based on a linear dynamical system, which is based on the Continuous […]. The post An Intuition about Kalman Filter for Computer Vision appeared first on Analytics Vidhya.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.

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How sklearn’s Tfidfvectorizer Calculates tf-idf Values

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Overview In NLP, tf-idf is an important measure and is used by algorithms like cosine similarity to find documents that are similar to a given search query. Here in this blog, we will try to break tf-idf and see how sklearn’s TfidfVectorizer calculates […]. The post How sklearn’s Tfidfvectorizer Calculates tf-idf Values appeared first on Analytics Vidhya.

NLP 313
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Deploying a Flask App on AWS Elastic Beanstalk

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Image 1- [link] Whether you are an experienced or an aspiring data scientist, you must have worked on machine learning model development comprising of data cleaning, wrangling, comparing different ML models, training the models on Python Notebooks like Jupyter. All the […]. The post Deploying a Flask App on AWS Elastic Beanstalk appeared first on Analytics Vidhya.

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Ensemble Modeling for Neural Networks using large datasets – Simplified!

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Ensemble modeling is the process by which a machine learning model combines distinct base models to generate generalized predictions using a combination of the predictive power of each of its components. Each base model differs with respect to the variable elements i.e. […].