Search results for "ec2"

119 documents found out of 7050 total.

• Setup and use JupyterHub (TLJH) on AWS EC2

  JupyterHub is a multi-user, container-friendly version of the Jupyter Notebook. However, it can be difficult to setup. This blog post will make you less likely to run into issues in this 15+ step process.

  https://www.kdnuggets.com/2023/01/setup-jupyterhub-tljh-aws-ec2.html

• Hosting Language Models on a Budget

  Learn how to run your own language model for free using lightweight models and Hugging Face Spaces.

  https://www.kdnuggets.com/hosting-language-models-on-a-budget

• 7 AWS Services for Machine Learning Projects

  Learn about the AWS machine learning service that helps you build machine learning pipelines, from processing data to training and deploying models.

  https://www.kdnuggets.com/7-aws-services-for-machine-learning-projects

• Building an Automatic Speech Recognition System with PyTorch & Hugging Face

  Check out this step-by-step guide to building a speech-to-text system with PyTorch & Hugging Face.

  https://www.kdnuggets.com/building-an-automatic-speech-recognition-system-with-pytorch-hugging-face

• How to Monitor Docker Containers

  This guide highlights the importance of container monitoring, key metrics to track, and tools ranging from Docker's built-in commands to comprehensive systems like Prometheus and Grafana.

  https://www.kdnuggets.com/how-to-monitor-docker-containers

• How to Train a Speech Recognition Model with Wav2Vec 2.0 and Hugging Face Transformers

  Let's learn how to train speech recognition model with Wav2Vec 2.0.

  https://www.kdnuggets.com/how-to-train-a-speech-recognition-model-with-wav2vec-2-0-and-hugging-face-transformers

• Tools Every AI Engineer Should Know: A Practical Guide

  Explore essential tools and skills for AI engineers: Python, R, big data frameworks, and cloud services essential for building and optimizing AI systems.

  https://www.kdnuggets.com/tools-every-ai-engineer-should-know-a-practical-guide

• 7 AI Portfolio Projects to Boost the Resume

  Get noticed by recruiters and hiring managers by creating and documenting the following AI projects.

  https://www.kdnuggets.com/7-ai-portfolio-projects-to-boost-the-resume

• Deploying Machine Learning Models: A Step-by-Step Tutorial

  Let us explore the process of deploying models in production.

  https://www.kdnuggets.com/deploying-machine-learning-models-a-step-by-step-tutorial

• Building a GPU Machine vs. Using the GPU Cloud

  The article examines the pros and cons of building an on-premise GPU machine versus using a GPU cloud service for projects involving deep learning and artificial intelligence, analyzing factors like cost, performance, operations, and scalability.

  https://www.kdnuggets.com/building-a-gpu-machine-vs-using-the-gpu-cloud

• Introduction to NExT-GPT: Any-to-Any Multimodal Large Language Model

  The future of the multimodal large language model.

  https://www.kdnuggets.com/introduction-to-nextgpt-anytoany-multimodal-large-language-model

• Getting Started with Google Cloud Platform in 5 Steps

  Explore the essentials of Google Cloud Platform for data science and ML, from account setup to model deployment, with hands-on project examples.

  https://www.kdnuggets.com/5-steps-google-cloud-platform

• Create a Dashboard Using Python and Dash

  The article explains how to build a Netflix dashboard with Python and Dash to visualize content distribution and classification using maps, charts, and graphs.

  https://www.kdnuggets.com/2023/08/create-dashboard-python-dash.html

• Parallel Processing Large File in Python

  Learn various techniques to reduce data processing time by using multiprocessing, joblib, and tqdm concurrent.

  https://www.kdnuggets.com/2022/07/parallel-processing-large-file-python.html

• KDnuggets News, January 25: ChatGPT as a Python Programming Assistant • Python and Machine Learning to Predict Football Match Winners

  ChatGPT as a Python Programming Assistant • How to Use Python and Machine Learning to Predict Football Match Winners • 20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 1 • From Data Collection to Model Deployment: 6 Stages of a Data Science Project • 5 Free Data Science Books You Must Read in 2023

  https://www.kdnuggets.com/2023/n03.html

• Beginner’s Guide to Cloud Computing

  Learn how cloud computing works, different types of models, top cloud platforms, and applications.

  https://www.kdnuggets.com/2023/01/beginner-guide-cloud-computing.html

• Key Data Science, Machine Learning, AI and Analytics Developments of 2022

  It's the end of the year, and so it's time for KDnuggets to assemble a team of experts and get to the bottom of what the most important data science, machine learning, AI and analytics developments of 2022 were.

  https://www.kdnuggets.com/2022/12/key-data-science-machine-learning-ai-analytics-developments-2022.html

• 5 Python Projects for Data Science Portfolio

  Get more experience by working on web scraping, data analytics, time-series forecasting, machine learning, and deep learning projects.

  https://www.kdnuggets.com/2022/12/5-python-projects-data-science-portfolio.html

• KDnuggets News, September 21: 7 Machine Learning Portfolio Projects to Boost the Resume • Free SQL and Database Course

  7 Machine Learning Portfolio Projects to Boost the Resume • Free SQL and Database Course • Top 5 Bookmarks Every Data Analyst Should Have • 7 Steps to Mastering Python for Data Science • 5 Concepts You Should Know About Gradient Descent and Cost Function

  https://www.kdnuggets.com/2022/n37.html

• 7 Machine Learning Portfolio Projects to Boost the Resume

  Work on machine learning and deep learning portfolio projects to learn new skills and improve your chance of getting hired.

  https://www.kdnuggets.com/2022/09/7-machine-learning-portfolio-projects-boost-resume.html

• The Complete Collection of Data Science Projects – Part 2

  The second part covers the list of Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Data Engineering, and MLOps.

  https://www.kdnuggets.com/2022/08/complete-collection-data-science-projects-part-2.html

• Data Transformation: Standardization vs Normalization

  Increasing accuracy in your models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature scaling methods of standardization and normalization, and demonstrates when and how to apply each approach.

  https://www.kdnuggets.com/2020/04/data-transformation-standardization-normalization.html

• Build a Machine Learning Web App in 5 Minutes

  In this article, you will learn to export your models and use them outside a Jupyter Notebook environment. You will build a simple web application that is able to feed user input into a machine learning model, and display an output prediction to the user.

  https://www.kdnuggets.com/2022/03/build-machine-learning-web-app-5-minutes.html

• 19 Data Science Project Ideas for Beginners

  This article features 19 data science projects for beginners, categorized into 7 full project tutorials, 5 places to come up with your own data science projects using data, and 7 skills-based data science projects.

  https://www.kdnuggets.com/2021/11/19-data-science-project-ideas-beginners.html

• Deploying a Streamlit WebApp to Heroku using DAGsHub

  Transform your machine learning models into a web app and share them with your friends and colleagues.

  https://www.kdnuggets.com/2022/02/deploying-streamlit-webapp-heroku-dagshub.html

• How to Speed Up XGBoost Model Training

  XGBoost is an open-source implementation of gradient boosting designed for speed and performance. However, even XGBoost training can sometimes be slow. This article will review the advantages and disadvantages of each approach as well as go over how to get started.

  https://www.kdnuggets.com/2021/12/speed-xgboost-model-training.html

• Build a Serverless News Data Pipeline using ML on AWS Cloud

  This is the guide on how to build a serverless data pipeline on AWS with a Machine Learning model deployed as a Sagemaker endpoint.

  https://www.kdnuggets.com/2021/11/build-serverless-news-data-pipeline-ml-aws-cloud.html

• How I Redesigned over 100 ETL into ELT Data Pipelines">How I Redesigned over 100 ETL into ELT Data Pipelines

  Learn how to level up your Data Pipelines!

  https://www.kdnuggets.com/2021/11/redesigned-over-100-etl-elt-data-pipelines.html

• Working with Python APIs For Data Science Project

  In this article, we will work with YouTube Python API to collect video statistics from our channel using the requests python library to make an API call and save it as a Pandas DataFrame.

  https://www.kdnuggets.com/2021/09/python-apis-data-science-project.html

• How to Create Stunning Web Apps for your Data Science Projects">How to Create Stunning Web Apps for your Data Science Projects

  Data scientists do not have to learn HTML, CSS, and JavaScript to build web pages.

  https://www.kdnuggets.com/2021/09/create-stunning-web-apps-data-science-projects.html

• Topic Modeling with Streamlit

  What does it take to create and deploy a topic modeling web application quickly? Read this post to see how the author uses Python NLP packages for topic modeling, Streamlit for the web application framework, and Streamlit Sharing for deployment.

  https://www.kdnuggets.com/2021/05/topic-modeling-streamlit.html

• Machine Translation in a Nutshell

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California for a snapshot of machine translation. Dr. Farzindar also provided the original art for this article.

  https://www.kdnuggets.com/2021/05/machine-translation-nutshell.html

• Software Engineering Best Practices for Data Scientists

  This is a crash course on how to bridge the gap between data science and software engineering.

  https://www.kdnuggets.com/2021/03/software-engineering-best-practices-data-scientists.html

• Beautiful decision tree visualizations with dtreeviz

  Improve the old way of plotting the decision trees and never go back!

  https://www.kdnuggets.com/2021/03/beautiful-decision-tree-visualizations-dtreeviz.html

• Dask and Pandas: No Such Thing as Too Much Data

  Do you love pandas, but don't love it when you reach the limits of your memory or compute resources? Dask provides you with the option to use the pandas API with distributed data and computing. Learn how it works, how to use it, and why it’s worth the switch when you need it most.

  https://www.kdnuggets.com/2021/03/dask-pandas-data.html

• Speech to Text with Wav2Vec 2.0

  Facebook recently introduced and open-sourced their new framework for self-supervised learning of representations from raw audio data called Wav2Vec 2.0. Learn more about it and how to use it here.

  https://www.kdnuggets.com/2021/03/speech-text-wav2vec.html

• Data Observability: Building Data Quality Monitors Using SQL

  To trigger an alert when data breaks, data teams can leverage a tried and true tactic from our friends in software engineering: monitoring and observability. In this article, we walk through how you can create your own data quality monitors for freshness and distribution from scratch using SQL.

  https://www.kdnuggets.com/2021/02/data-observability-building-data-quality-monitors-using-sql.html

• Build Your First Data Science Application">Build Your First Data Science Application

  Check out these seven Python libraries to make your first data science MVP application.

  https://www.kdnuggets.com/2021/02/build-first-data-science-application.html

• Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants">Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants

  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.

  https://www.kdnuggets.com/2021/01/cloud-computing-data-science-ml-trends-2020-2022-battle-giants.html

• Mastering TensorFlow Variables in 5 Easy Steps

  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.

  https://www.kdnuggets.com/2021/01/mastering-tensorflow-variables-5-easy-steps.html

• How to Get a Job as a Data Engineer

  Data engineering skills are currently in high demand. If you are looking for career prospects in this fast-growing profession, then these 10 skills and key factors will help you prepare to land an entry-level position in this field.

  https://www.kdnuggets.com/2021/01/get-job-as-data-engineer.html

• 5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use

  If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.

  https://www.kdnuggets.com/2020/11/5-useful-machine-learning-tools.html

• Is Data Science for Me? 14 Self-examination Questions to Consider">Is Data Science for Me? 14 Self-examination Questions to Consider

  You are intrigued by this exciting new field of Data Science, and you think you want in on the action. The demand remains very high and the salaries are strong. Before taking the leap onto this path, these questions will help you evaluate if you are ready for the challenges and opportunities.

  https://www.kdnuggets.com/2020/11/data-science-14-self-examination-questions.html

• Mastering TensorFlow Tensors in 5 Easy Steps

  Discover how the building blocks of TensorFlow works at the lower level and learn how to make the most of Tensor objects.

  https://www.kdnuggets.com/2020/11/mastering-tensorflow-tensors-5-easy-steps.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

• Deploying Streamlit Apps Using Streamlit Sharing

  Read this sneak peek into Streamlit’s new deployment platform.

  https://www.kdnuggets.com/2020/10/deploying-streamlit-apps-streamlit-sharing.html

• KDD-2020 (virtual), the leading conference on Data Science and Knowledge Discovery, Aug 23-27 – register now

  Using an interactive VR platform, KDD-2020 brings you the latest research in AI, Data Science, Deep Learning, and Machine Learning with tutorials to improve your skills, keynotes from top experts, workshops on state-of-the-art topics and over 200 research presentations.

  https://www.kdnuggets.com/2020/08/kdd-2020-virtual-august.html

• Deploy Machine Learning Pipeline on AWS Fargate">Deploy Machine Learning Pipeline on AWS Fargate

  A step-by-step beginner’s guide to containerize and deploy ML pipeline serverless on AWS Fargate.

  https://www.kdnuggets.com/2020/07/deploy-machine-learning-pipeline-aws-fargate.html

• The Most Important Fundamentals of PyTorch you Should Know">The Most Important Fundamentals of PyTorch you Should Know

  PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.

  https://www.kdnuggets.com/2020/06/fundamentals-pytorch.html

• Top 10 Data Visualization Tools for Every Data Scientist">Top 10 Data Visualization Tools for Every Data Scientist

  At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.

  https://www.kdnuggets.com/2020/05/top-10-data-visualization-tools-every-data-scientist.html

• A Layman’s Guide to Data Science. Part 2: How to Build a Data Project

  As Part 2 in a Guide to Data Science, we outline the steps to build your first Data Science project, including how to ask good questions to understand the data first, how to prepare the data, how to develop an MVP, reiterate to build a good product, and, finally, present your project.

  https://www.kdnuggets.com/2020/04/guide-data-science-build-data-project.html

• Microsoft Research Uses Transfer Learning to Train Real-World Autonomous Drones

  The new research uses policies learned in simulations in real world drone environments.

  https://www.kdnuggets.com/2020/03/microsoft-research-transfer-learning-train-real-world-autonomous-drones.html

• ModelDB 2.0 is here!

  We are excited to announce that ModelDB 2.0 is now available! We have learned a lot since building ModelDB 1.0, so we decided to rebuild from the ground up.

  https://www.kdnuggets.com/2020/03/verta-modeldb-20.html

• The Most Useful Machine Learning Tools of 2020

  This articles outlines 5 sets of tools every lazy full-stack data scientist should use.

  https://www.kdnuggets.com/2020/03/most-useful-machine-learning-tools-2020.html

• Decision Boundary for a Series of Machine Learning Models

  I train a series of Machine Learning models using the iris dataset, construct synthetic data from the extreme points within the data and test a number of Machine Learning models in order to draw the decision boundaries from which the models make predictions in a 2D space, which is useful for illustrative purposes and understanding on how different Machine Learning models make predictions.

  https://www.kdnuggets.com/2020/03/decision-boundary-series-machine-learning-models.html

• The Augmented Scientist Part 1: Practical Application Machine Learning in Classification of SEM Images

  Our goal here is to see if we can build a classifier that can identify patterns in Scanning Electron Microscope (SEM) images, and compare the performance of our classifier to the current state-of-the-art.

  https://www.kdnuggets.com/2020/03/the-augmented-scientist-practical-application-machine-learning-classification-images.html

• How to learn data science on your own: a practical guide

  While much focus today is on the rise in working from home and the challenges experienced, not as much is said about learning from home. For those lone wolfs studying Data Science in a self-directed way, a range of issues can get in the way of your goal. Learn about these common problems to prepare to focus yourself all the way to your educational goals.

  https://www.kdnuggets.com/2020/02/learn-data-science-guide.html

• Amazon Gets Into the AutoML Race with AutoGluon: Some AutoML Architectures You Should Know About

  Amazon, Microsoft, Salesforce, Waymo have produced some of the most innovative AutoML architectures in the market.

  https://www.kdnuggets.com/2020/01/amazon-automl-autogluon-architectures-know-about.html

• Deploying a pretrained GPT-2 model on AWS

  This post attempts to summarize my recent detour into NLP, describing how I exposed a Huggingface pre-trained Language Model (LM) on an AWS-based web application.

  https://www.kdnuggets.com/2019/12/deploying-pretrained-gpt-2-model-aws.html

• A Gentle Introduction to PyTorch 1.2

  This comprehensive tutorial aims to introduce the fundamentals of PyTorch building blocks for training neural networks.

  https://www.kdnuggets.com/2019/09/gentle-introduction-pytorch-12.html

• Train sklearn 100x Faster">Train sklearn 100x Faster

  As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

  https://www.kdnuggets.com/2019/09/train-sklearn-100x-faster.html

• Pytorch Lightning vs PyTorch Ignite vs Fast.ai

  Here, I will attempt an objective comparison between all three frameworks. This comparison comes from laying out similarities and differences objectively found in tutorials and documentation of all three frameworks.

  https://www.kdnuggets.com/2019/08/pytorch-lightning-vs-pytorch-ignite-vs-fast-ai.html

• Can we trust AutoML to go on full autopilot?

  We put an AutoML tool to the test on a real-world problem, and the results are surprising. Even with automatic machine learning, you still need expert data scientists.

  https://www.kdnuggets.com/2019/07/automl-full-autopilot.html

• Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras">Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras

  Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.

  https://www.kdnuggets.com/2019/07/convolutional-neural-networks-python-tutorial-tensorflow-keras.html

• The Hackathon Guide for Aspiring Data Scientists">The Hackathon Guide for Aspiring Data Scientists

  This article is an overview of how to prepare for a hackathon as an aspiring data scientist, highlighting the 4 reasons why you should take part in one, along with a series of tips for participation.

  https://www.kdnuggets.com/2019/07/hackathon-guide-aspiring-data-scientists.html

• Deploy your PyTorch model to Production

  This tutorial aims to teach you how to deploy your recently trained model in PyTorch as an API using Python.

  https://www.kdnuggets.com/2019/03/deploy-pytorch-model-production.html

• Automated Machine Learning in Python

  An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.

  https://www.kdnuggets.com/2019/01/automated-machine-learning-python.html

• Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU

  A detailed comparison of the best places to train your deep learning model for the lowest cost and hassle, including AWS, Google, Paperspace, vast.ai, and more.

  https://www.kdnuggets.com/2018/11/deep-learning-cloud-providers-cpu-gpu-tpu.html

• Text Preprocessing in Python: Steps, Tools, and Examples

  We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.

  https://www.kdnuggets.com/2018/11/text-preprocessing-python.html

• Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi

  Read this post to learn how to build a surveillance system using a USB camera plugged into Raspberry Pi (RPi) which is connected a PC using its wireless interface.

  https://www.kdnuggets.com/2018/11/building-surveillance-system-usb-camera-wireless-connected-raspberry-pi.html

• Introduction to Deep Learning

  I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.

  https://www.kdnuggets.com/2018/09/introduction-deep-learning.html

• Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP">Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP

  Knowledge about the structure and syntax of language is helpful in many areas like text processing, annotation, and parsing for further operations such as text classification or summarization.

  https://www.kdnuggets.com/2018/08/understanding-language-syntax-and-structure-practitioners-guide-nlp-3.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

• IoT on AWS: Machine Learning Models and Dashboards from Sensor Data

  I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.

  https://www.kdnuggets.com/2018/06/zimbres-iot-aws-machine-learning-dashboard.html

• Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API">Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API

  In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.

  https://www.kdnuggets.com/2018/05/complete-guide-convnet-tensorflow-flask-restful-python-api.html

• Blockchain Explained in 7 Python Functions">Blockchain Explained in 7 Python Functions

  It wasn’t until I wrote my own simple Blockchain, that I truly understood what it is and the potential applications for it. So without further ado, lets set up our 7 functions!

  https://www.kdnuggets.com/2018/04/blockchain-explained-7-python-functions.html

• How StockTwits Applies Social and Sentiment Data Science

  StockTwits is a social network for investors and traders, giving them a platform to share assertions and perceptions, analyses and predictions.

  https://www.kdnuggets.com/2018/03/stocktwits-social-sentiment-data-science.html

• A Day in the Life of an AI Developer">A Day in the Life of an AI Developer

  This is the narrative of a typical AI Sunday, where I decided to look at building a sequence to sequence (seq2seq) model based chatbot using some already available sample code and data from the Cornell movie database.

  https://www.kdnuggets.com/2018/01/day-life-ai-developer.html

• 70 Amazing Free Data Sources You Should Know">70 Amazing Free Data Sources You Should Know

  70 free data sources for 2017 on government, crime, health, financial and economic data, marketing and social media, journalism and media, real estate, company directory and review, and more to start working on your data projects.

  https://www.kdnuggets.com/2017/12/big-data-free-sources.html

• Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras">Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

  We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.

  https://www.kdnuggets.com/2017/11/understanding-deep-convolutional-neural-networks-tensorflow-keras.html

• Visualizing High Dimensional Data In Augmented Reality

  When Data Scientists first get a data set, they oftne use a matrix of 2D scatter plots to quickly see the contents and relationships between pairs of attributes. But for data with lots of attributes, such analysis does not scale.

  https://www.kdnuggets.com/2017/09/ibm-visualizing-high-dimensional-data-augmented-reality.html

• First Steps of Learning Deep Learning: Image Classification in Keras

  Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!

  https://www.kdnuggets.com/2017/08/first-steps-learning-deep-learning-image-classification-keras.html

• Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners, part 2">Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners, part 2

  Here are deep learning examples and demos you can just download and run, including Spotify Artist Search using Speech APIs, Symbolic AI Speech Recognition, and Algorithmia API Photo Colorizer.

  https://www.kdnuggets.com/2017/07/deep-learning-demos-code-beginners-part2.html

• Introducing Dask-SearchCV: Distributed hyperparameter optimization with Scikit-Learn

  We introduce a new library for doing distributed hyperparameter optimization with Scikit-Learn estimators. We compare it to the existing Scikit-Learn implementations, and discuss when it may be useful compared to other approaches.

  https://www.kdnuggets.com/2017/05/dask-searchcv-distributed-hyperparameter-optimization-scikit-learn.html

• The Internet of Things in the Cloud

  Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.

  https://www.kdnuggets.com/2017/05/internet-of-things-iot-cloud.html

• Data Science & Machine Learning Platforms for the Enterprise

  A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.

  https://www.kdnuggets.com/2017/05/data-science-machine-learning-platforms-enterprise.html

• 42 Essential Quotes by Data Science Thought Leaders

  42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.

  https://www.kdnuggets.com/2017/05/42-essential-quotes-data-science-thought-leaders.html

• Dask and Pandas and XGBoost: Playing nicely between distributed systems

  This blogpost gives a quick example using Dask.dataframe to do distributed Pandas data wrangling, then using a new dask-xgboost package to setup an XGBoost cluster inside the Dask cluster and perform the handoff.

  https://www.kdnuggets.com/2017/04/dask-pandas-xgboost-playing-nicely-distributed-systems.html

• Greed, Fear, Game Theory and Deep Learning

  The most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. The core problem of a multi-agent approach is how to control its behavior.

  https://www.kdnuggets.com/2017/03/greed-fear-game-theory-deep-learning.html

• Data Science of Sales Calls: 3 Actionable Findings

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  https://www.kdnuggets.com/2016/01/data-science-resume-tips-guidelines.html

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  https://www.kdnuggets.com/2015/04/hadoop-as-service-18-cloud-options.html

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