KDnuggets™ News 20:n26, Jul 8: Speed up Your Numpy and Pandas; A Layman’s Guide to Data Science; Getting Started with TensorFlow 2
Speed up your Numpy and Pandas with NumExpr Package; A Layman's Guide to Data Science. Part 3: Data Science Workflow; Getting Started with TensorFlow 2; Feature Engineering in SQL and Python: A Hybrid Approach; Deploy Machine Learning Pipeline on AWS Fargate
Features | News | Tutorials | Opinions | Tops | Jobs | Submit a blog | Image of the week
Features
News
Tutorials, Overviews
Opinions
Top Stories, Tweets
Jobs
Image of the week
This week: We show how to significantly speed up your mathematical calculations in Numpy and Pandas using a small library; Learn and appreciate the typical workflow for a data science project, including data preparation, analysis, reflection, and communication of the results; Learn about the latest version of TensorFlow with a hands-on walk-through of implementing a classification problem with deep learning; See a hybrid approach to feature engineering in SQL and Python; Read a step-by-step beginner's guide to containerize and deploy ML pipeline serverless on AWS Fargate.
Features
Speed up your Numpy and Pandas with NumExpr Package
- A Layman's Guide to Data Science. Part 3: Data Science Workflow
- Getting Started with TensorFlow 2
- Feature Engineering in SQL and Python: A Hybrid Approach
- Deploy Machine Learning Pipeline on AWS Fargate
Speed up your Numpy and Pandas with NumExpr Package
Getting Started with TensorFlow 2News
- 5th International Summer School 2020 on Resource-aware Machine Learning (REAML)
- Innovating versus Doing: NLP and CORD19
- Lynx Analytics is open-sourcing LynxKite, its Complete Graph Data Science Platform
- Five Lines of Code
Tutorials, Overviews
- Some Things Uber Learned from Running Machine Learning at Scale
- A Complete Guide To Survival Analysis In Python, part 1
- PyTorch for Deep Learning: The Free eBook
- Exploratory Data Analysis on Steroids
- Generating cooking recipes using TensorFlow and LSTM Recurrent Neural Network: A step-by-step guide
- PyTorch Multi-GPU Metrics Library and More in New PyTorch Lightning Release
- Data Cleaning: The secret ingredient to the success of any Data Science Project
- Software engineering fundamentals for Data Scientists
- How to Prepare Your Data
- Learn Data Science from Top Universities for Free
- An Introduction to Statistical Learning: The Free eBook
- Practical Markov Chain Monte Carlo
- Free Economics & Finance Courses for Data Scientists
- Build a Branded Web Based GIS Application Using R, Leaflet and Flexdashboard
- The 8 Basic Statistics Concepts for Data Science
- Time Complexity: How to measure the efficiency of algorithms
Opinions
- Scope and Impact of AI in Agriculture
- Data Scientists Have Developed a Faster Way to Reduce Pollution, Cut Greenhouse Gas Emissions
- Largest Dataset Analyzed - Poll Results and Trends
- How to Build Your Data Science Competency for Post-COVID Future
- Stop training more models, start deploying them
- The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)
How Much Math do you need in Data Science?
- Exploring the Real World of Data Science
- Data Science Tools Popularity, animated
- Learning by Forgetting: Deep Neural Networks and the Jennifer Aniston Neuron
- Machine Learning Engineer vs Data Scientist (Is Data Science Over?)
How Much Math do you need in Data Science?
Learning by Forgetting: Deep Neural Networks and the Jennifer Aniston NeuronTop Stories, Tweets
- Top Stories, Jun 29 - Jul 5: Speed up your Numpy and Pandas with NumExpr Package; Deploy Machine Learning Pipeline on AWS Fargate
- Top Stories, Jun 22-28: How Much Math do you need in Data Science?
- Top KDnuggets tweets, Jun 17-23: The Best NLP with Deep Learning Course is Free
Jobs
- See our recent jobs in AI, Analytics, Data Science, Machine Learning
- You can post a free short entry on KDnuggets jobs page for an industry or academic job related to AI, Big Data, Data Science, or Machine Learning, email - see details at kdnuggets.com/jobs
Image of the week
From A Layman’s Guide to Data Science. Part 3: Data Science Workflow
