Search results for python 3.7

Found 57 documents, 5937 searched:

• Introduction to Memory Profiling in Python

  So where did all the memory go? To figure out, learn how to profile your Python code for memory usage using the memory-profiler package.

  https://www.kdnuggets.com/introduction-to-memory-profiling-in-python

• How To Speed Up SQL Queries Using Indexes [Python Edition]

  Learn to work with SQLite databases using Python’s built-in sqlite3 module. Also learn how to create indexes to speed up queries.

  https://www.kdnuggets.com/2023/08/speed-sql-queries-indexes-python-edition.html

• How to Determine the Best Fitting Data Distribution Using Python

  Approaches to data sampling, modeling, and analysis can vary based on the distribution of your data, and so determining the best fit theoretical distribution can be an essential step in your data exploration process.

  https://www.kdnuggets.com/2021/09/determine-best-fitting-data-distribution-python.html

• Python Data Structures Compared

  Let's take a look at 5 different Python data structures and see how they could be used to store data we might be processing in our everyday tasks, as well as the relative memory they use for storage and time they take to create and access.

  https://www.kdnuggets.com/2021/07/python-data-structures-compared.html

• 10 Python Skills They Don’t Teach in Bootcamp

  Ascend to new heights in Data Science and Machine Learning with this thrilling list of coding tips.

  https://www.kdnuggets.com/2020/12/10-python-skills-dont-teach-bootcamp.html

• fastcore: An Underrated Python Library">fastcore: An Underrated Python Library

  A unique python library that extends the python programming language and provides utilities that enhance productivity.

  https://www.kdnuggets.com/2020/10/fastcore-underrated-python-library.html

• Here are the Most Popular Python IDEs/Editors

  Jupyter Notebook continues to lead as the most popular Python IDE, but its share has declined since the last poll. The top 4 contenders have remained the same, but only one has significantly improved its share. We also examine the breakdown by employment and region.

  https://www.kdnuggets.com/2020/10/most-popular-python-ides-editors.html

• Audio Data Analysis Using Deep Learning with Python (Part 2)

  This is a followup to the first article in this series. Once you are comfortable with the concepts explained in that article, you can come back and continue with this.

  https://www.kdnuggets.com/2020/02/audio-data-analysis-deep-learning-python-part-2.html

• An Overview of Python’s Datatable package

  Modern machine learning applications need to process a humongous amount of data and generate multiple features. Python’s datatable module was created to address this issue. It is a toolkit for performing big data (up to 100GB) operations on a single-node machine, at the maximum possible speed.

  https://www.kdnuggets.com/2019/08/overview-python-datatable-package.html

• Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis">Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis

  Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.

  https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html

• Here are the most popular Python IDEs / Editors">Here are the most popular Python IDEs / Editors

  We report on the most popular IDE and Editors, based on our poll. Jupyter is the favorite across all regions and employment types, but there is competition for no. 2 and no. 3 spots.

  https://www.kdnuggets.com/2018/12/most-popular-python-ide-editor.html

• Genetic Algorithm Implementation in Python">Genetic Algorithm Implementation in Python

  This tutorial will implement the genetic algorithm optimization technique in Python based on a simple example in which we are trying to maximize the output of an equation.

  https://www.kdnuggets.com/2018/07/genetic-algorithm-implementation-python.html

• Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis">Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis

  Python continues to eat away at R, RapidMiner gains, SQL is steady, Tensorflow advances pulling along Keras, Hadoop drops, Data Science platforms consolidate, and more.

  https://www.kdnuggets.com/2018/05/poll-tools-analytics-data-science-machine-learning-results.html

• Web Scraping Tutorial with Python: Tips and Tricks">Web Scraping Tutorial with Python: Tips and Tricks

  This post is intended for people who are interested to know about the common design patterns, pitfalls and rules related to the web scraping.

  https://www.kdnuggets.com/2018/02/web-scraping-tutorial-python.html

• Comparing Distance Measurements with Python and SciPy

  This post introduces five perfectly valid ways of measuring distances between data points. We will also perform simple demonstration and comparison with Python and the SciPy library.

  https://www.kdnuggets.com/2017/08/comparing-distance-measurements-python-scipy.html

• R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results

  R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science platform. Big Data tools used by almost 40%, and Deep Learning usage doubles.

  https://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html

• R leads RapidMiner, Python catches up, Big Data tools grow, Spark ignites

  R is the most popular overall tool among data miners, although Python usage is growing faster. RapidMiner continues to be most popular suite for data mining/data science. Hadoop/Big Data tools usage grew to 29%, propelled by 3x growth in Spark. Other tools with strong growth include H2O (0xdata), Actian, MLlib, and Alteryx.

  https://www.kdnuggets.com/2015/05/poll-r-rapidminer-python-big-data-spark.html

• Getting Started with PyCaret

  An open-source low-code machine learning library for training and deploying the models in production.

  https://www.kdnuggets.com/2022/11/getting-started-pycaret.html

• Overview of AutoNLP from Hugging Face with Example Project

  AutoNLP is a beta project from Hugging Face that builds on the company’s work with its Transformer project. With AutoNLP you can get a working model with just a few simple terminal commands.

  https://www.kdnuggets.com/2021/06/overview-autonlp-hugging-face-example-project.html

• Build Your Own AutoML Using PyCaret 2.0

  In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs.

  https://www.kdnuggets.com/2020/08/build-automl-pycaret.html

• Apache Spark Cluster on Docker

  Build your own Apache Spark cluster in standalone mode on Docker with a JupyterLab interface.

  https://www.kdnuggets.com/2020/07/apache-spark-cluster-docker.html

• Machine Learning in Power BI using PyCaret

  Check out this step-by-step tutorial for implementing machine learning in Power BI within minutes.

  https://www.kdnuggets.com/2020/05/machine-learning-power-bi-pycaret.html

• Build an app to generate photorealistic faces using TensorFlow and Streamlit

  We’ll show you how to quickly build a Streamlit app to synthesize celebrity faces using GANs, Tensorflow, and st.cache.

  https://www.kdnuggets.com/2020/04/app-generate-photorealistic-faces-tensorflow-streamlit.html

• Using RAPIDS cuDF to Leverage GPU in Feature Engineering

  Improving Performance by Replacing Pandas with cuDF in Creating Data Frames and Engineering Features and Integrating with Google Colab.

  https://www.kdnuggets.com/2023/06/rapids-cudf-leverage-gpu-feature-engineering.html

• Solving 5 Complex SQL Problems: Tricky Queries Explained

  The 5 hardest things Josh Berry, a 15 year analytics professional, experienced while switching from Python to SQL. Offering examples, SQL code, and a resource to customize the SQL to your own project.

  https://www.kdnuggets.com/2022/07/5-hardest-things-sql.html

• 3 Approaches to Data Imputation

  Learn about data imputation and 3 ways in which to implement it using Python.

  https://www.kdnuggets.com/2022/12/3-approaches-data-imputation.html

• Top 18 Data Science Facebook Groups

  Join the best data science groups on Facebook to share insights and experiences, ask for guidance, and build valuable connections.

  https://www.kdnuggets.com/2022/06/top-18-data-science-facebook-groups.html

• Top 7 YouTube Courses on Data Analytics

  Learn data analytics by taking the best YouTube courses. These courses will cover data analysis with Python, R, SQL, PowerBI, Tableau, Excel, and SPSS.

  https://www.kdnuggets.com/2022/02/top-7-youtube-courses-data-analytics.html

• Real Time Image Segmentation Using 5 Lines of Code

  PixelLib Library is a library created to allow easy integration of object segmentation in images and videos using few lines of python code. PixelLib now provides support for PyTorch backend to perform faster, more accurate segmentation and extraction of objects in images and videos using PointRend segmentation architecture.

  https://www.kdnuggets.com/2021/10/real-time-image-segmentation-5-lines-code.html

• Deploying Your First Machine Learning API">Deploying Your First Machine Learning API

  Effortless way to develop and deploy your machine learning API using FastAPI and Deta.

  https://www.kdnuggets.com/2021/10/deploying-first-machine-learning-api.html

• AutoML: An Introduction Using Auto-Sklearn and Auto-PyTorch

  AutoML is a broad category of techniques and tools for applying automated search to your automated search and learning to your learning. In addition to Auto-Sklearn, the Freiburg-Hannover AutoML group has also developed an Auto-PyTorch library. We’ll use both of these as our entry point into AutoML in the following simple tutorial.

  https://www.kdnuggets.com/2021/10/automl-introduction-auto-sklearn-auto-pytorch.html

• Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics">Not Only for Deep Learning: How GPUs Accelerate Data Science & Data Analytics

  Modern AI/ML systems’ success has been critically dependent on their ability to process massive amounts of raw data in a parallel fashion using task-optimized hardware. Can we leverage the power of GPU and distributed computing for regular data processing jobs too?

  https://www.kdnuggets.com/2021/07/deep-learning-gpu-accelerate-data-science-data-analytics.html

• How to Dockerize Any Machine Learning Application

  How can you -- an awesome Data Scientist -- also be known as an awesome software engineer? Docker. And these 3 simple steps to use it for your solutions over and over again.

  https://www.kdnuggets.com/2021/04/dockerize-any-machine-learning-application.html

• Top YouTube Machine Learning Channels

  These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.

  https://www.kdnuggets.com/2021/03/top-youtube-machine-learning-channels.html

• Machine learning is going real-time

  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?

  https://www.kdnuggets.com/2021/01/machine-learning-real-time.html

• Deploying Secure and Scalable Streamlit Apps on AWS with Docker Swarm, Traefik and Keycloak

  If you are a data scientist who just wants to get the work done but doesn’t necessarily want to go down the DevOps rabbit hole, this tutorial offers a relatively straightforward deployment solution leveraging Docker Swarm and Traefik, with an option of adding user authentication with Keycloak.

  https://www.kdnuggets.com/2020/10/deploying-secure-scalable-streamlit-apps-aws-docker-swarm-traefik-keycloak.html

• GitHub is the Best AutoML You Will Ever Need

  This article uses PyCaret 2.0, an open source, low-code machine learning library in Python to develop a simple AutoML solution and deploy it as a Docker container using GitHub actions.

  https://www.kdnuggets.com/2020/08/github-best-automl-ever-need.html

• Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide

  A character-level LSTM (Long short-term memory) RNN (Recurrent Neural Network) is trained on ~100k recipes dataset using TensorFlow. The model suggested the recipes "Cream Soda with Onions", "Puff Pastry Strawberry Soup", "Zucchini flavor Tea", and "Salmon Mousse of Beef and Stilton Salad with Jalapenos". Yum!? Follow along this detailed guide with code to create your own recipe-generating chef.

  https://www.kdnuggets.com/2020/07/generating-cooking-recipes-using-tensorflow.html

• Free Data Analytics Courses

  Wherever your skills are at today, check out these top course recommendations for 2020 to help you master data analytics.

  https://www.kdnuggets.com/2020/06/free-data-analytics-courses.html

• Geovisualization with Open Data

  In this post I want to show how to use public available (open) data to create geo visualizations in python. Maps are a great way to communicate and compare information when working with geolocation data. There are many frameworks to plot maps, here I focus on matplotlib and geopandas (and give a glimpse of mplleaflet).

  https://www.kdnuggets.com/2020/01/open-data-germany-maps-viz.html

• A 2019 Guide to Object Detection

  Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well.

  https://www.kdnuggets.com/2019/08/2019-guide-object-detection.html

• XGBoost and Random Forest® with Bayesian Optimisation

  This article will explain how to use XGBoost and Random Forest with Bayesian Optimisation, and will discuss the main pros and cons of these methods.

  https://www.kdnuggets.com/2019/07/xgboost-random-forest-bayesian-optimisation.html

• Building a Recommender System, Part 2

  This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.

  https://www.kdnuggets.com/2019/07/building-recommender-system-part-2.html

• How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms

  We outline three different clustering algorithms - k-means clustering, hierarchical clustering and Graph Community Detection - providing an explanation on when to use each, how they work and a worked example.

  https://www.kdnuggets.com/2019/04/introduction-clustering-algorithms.html

• How to build an API for a machine learning model in 5 minutes using Flask">How to build an API for a machine learning model in 5 minutes using Flask

  Flask is a micro web framework written in Python. It can create a REST API that allows you to send data, and receive a prediction as a response.

  https://www.kdnuggets.com/2019/01/build-api-machine-learning-model-using-flask.html

• Overview and benchmark of traditional and deep learning models in text classification

  In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.

  https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.html

• Why You Should Start Using .npy Files More Often

  In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.

  https://www.kdnuggets.com/2018/04/start-using-npy-files-more-often.html

• Is Learning Rate Useful in Artificial Neural Networks?

  This article will help you understand why we need the learning rate and whether it is useful or not for training an artificial neural network. Using a very simple Python code for a single layer perceptron, the learning rate value will get changed to catch its idea.

  https://www.kdnuggets.com/2018/01/learning-rate-useful-neural-network.html

• Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated">Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated

  Looking for advice? Guidance? Stories? We’ve put a list of the top ten LinkedIn influencers of the last three months, follow them and stay up-to-date with the latest news in Big Data, Data Science, Analytics, Machine Learning and AI.

  https://www.kdnuggets.com/2017/09/top-10-big-data-science-machine-learning-influencers-linkedin-updated.html

• New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll">New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll

  Python caught up with R and (barely) overtook it; Deep Learning usage surges to 32%; RapidMiner remains top general Data Science platform; Five languages of Data Science.
   
   

  https://www.kdnuggets.com/2017/05/poll-analytics-data-science-machine-learning-software-leaders.html

• Applying Machine Learning To March Madness

  March Madness is upon us. But before you get your brackets set, check out this overview of using machine learning to do the heavy lifting for you. A great discussion, and a timely topic.

  https://www.kdnuggets.com/2017/03/machine-learning-march-madness.html

• Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition

  Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.

  https://www.kdnuggets.com/2016/11/top-10-amazon-books-ai-machine-learning.html

• Top 10 Amazon Books in Data Mining, 2016 Edition">Top 10 Amazon Books in Data Mining, 2016 Edition

  Given the ongoing explosion in interest for all things Data Mining, Data Science, Analytics, Big Data, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Data Mining category.

  https://www.kdnuggets.com/2016/11/top-10-amazon-books-data-mining.html

• Four main languages for Analytics, Data Mining, Data Science

  New KDnuggets Poll shows the growing dominance of four main languages for Analytics, Data Mining, and Data Science: R, SAS, Python, and SQL - used by 91% of data scientists - and decline in popularity of other languages, except for Julia and Scala.

  https://www.kdnuggets.com/2014/08/four-main-languages-analytics-data-mining-data-science.html

• KDnuggets Analytics, Data Mining, Data Science Software Poll – Analyzed

  We analyze the results of KDnuggets Software Poll, including correlations between tools, and relationships between commercial, free, and Hadoop/Big Data tools. We identify a potential capability gap. Download anonymized data and analyze it yourself.

  https://www.kdnuggets.com/2014/06/analytics-data-mining-data-science-software-poll-analyzed.html

• KDnuggets 15th Annual Analytics, Data Mining, Data Science Software Poll: RapidMiner Continues To Lead

  With over 3,000 data miners taking part in KDnuggets 15th Annual Software Poll, RapidMiner continues to lead. Free software is used much more outside US, and Hadoop usage grows fastest in Asia.

  https://www.kdnuggets.com/2014/06/kdnuggets-annual-software-poll-rapidminer-continues-lead.html

• KDnuggets Annual Software Poll:RapidMiner and R vie for first place

  The 2013 KDnuggets Software Poll was marked by a battle between RapidMiner and R for the first place. Surprisingly, commercial and free software maintained parity, with about 30% using each exclusively, and 40% using both. Only 10% used their own code - is analytics software maturing? Real Big Data is still done by a minority - only 1 in 7 used Hadoop or similar tools, same as last year.

  https://www.kdnuggets.com/2013/06/kdnuggets-annual-software-poll-rapidminer-r-vie-for-first-place.html