🐼🤹‍♂️ pandas trick:
Two easy ways to reduce DataFrame memory usage:
1. Only read in columns you need
2. Use 'category' data type with categorical data

Example:
df = pd.read_csv('file.csv', usecols=['A', 'C', 'D'], dtype={'D':'category'})

#Python #DataScience

✴️ @AI_Python_EN

Quick links for all things #R and #Python:

1. Overview of using python with RStudio: https://lnkd.in/d5NkJAt
2. Python & #shiny: https://lnkd.in/dVfkE6b
3. Python & #rmarkdown: https://lnkd.in/dXpSd7i
4. Python with #plumber: https://lnkd.in/dn2pEAQ

For a central location to publish all of your team's data products (R artifacts, R & python mixed assets, and #jupyternotebooks), check out RStudio Connect: https://lnkd.in/dXW7iPG

✴️ @AI_Python_EN

#Python, Performance, and GPUs

https://towardsdatascience.com/python-performance-and-gpus-1be860ffd58d

✴️ @AI_Python_EN

Getting System Information in Linux using Python Script.
#BigData #Analytics #DataScience #IoT #PyTorch #Python #RStats #TensorFlow #DataScientist #Linux

http://bit.ly/2X56cZa

✴️ @AI_Python_EN

#AI/ #DataScience/ #MachineLearning/ #ML:
7 Steps for Data Preparation Using #Python

Link => https://bit.ly/PyDataPrep

#datamining #statistics #bigdata #artificialintelligence

✴️ @AI_Python_EN

Module 3: Core Machine Learning (May-October Semester)
July 6th by FAST-NU AI/ML Training Center

Module 3 (Core Machine Learning) of our ongoing cohort (May October semester) for the AI-ML training program. It covers basic to intermediate Machine Learning and lays a solid foundation to build or transition into a career of ML and Data Science, and also to provide a thorough grounding for the next Deep Learning Module.

https://www.facebook.com/events/2195319697439547/

#deeplearning #machinelearning #opencv #AI #ML #Python

✴️ @AI_Python_EN

10 new posts on datahacker.rs.
Introduction to #ComputerVision using #OpenCV (#Python and #C++)

https://lnkd.in/gZtj_g6

✴️ @AI_Python_EN

Welcome to @ai_machinelearning_big_data the world of :
* #Artificial #Intelligence,
* #Deep #Learning,
* #Machine #Learning,
* #Data #Science
* #Python Programming language
* and more advanced research
links🔗 and more you wanted.
Join us and learn hot topics of Computer Science together.👇👇👇

@ai_machinelearning_big_data

Classifying Legendary Pokemon Birds 🐦🐦🐦

👉👉👉 Try it yourself:
https://lnkd.in/eYhKNAh 👈👈👈

After only the second fastai "Practical Deep Learning for Coders" class I was able to complete an end-to-end deep learning project! 🤖🤖🤖

The main goal is to classify an image as either one of the Legendary Pokemon Birds - Articuno, Moltres or Zapdos - or an alternative class which includes everything else. Needless to say that sometimes my model gets confused about the alternative class since not so many diverse images were feed into it...

Source code:
https://lnkd.in/eRfkBx8
Forked from:
https://lnkd.in/e_k4nqN

#ai #ml #dl #deeplearning #cnn #python

✴️ @AI_Python_EN

Pytorch Implementation of Deep Flow.

#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #DataScientist #Linux

https://arxiv.org/pdf/1905.02884.pdf

✴️ @AI_Python_EN

DYC is a CLI tool that helps with documenting your #python source code. It will help keep you alert for new methods that were added and not documented. Also supports to build a reusable docstring template. Just answer the prompt questions in your terminal to see the effect on your files.

https://github.com/Zarad1993/dyc

Understanding the Backpropagation Algorithm.

#BigData #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #Algorithms

http://bit.ly/2ASKwqx

❇️ @AI_Python_EN

than standard Adam? - I ran 24 experiments to find out. - The answer? Meh, not really. Full tutorial w/ #Python code here:
http://pyimg.co/asash

#DeepLearning #Keras #MachineLearning #ArtificialIntelligence #AI #DataScience

❇️ @AI_Python_en

new #python package #imagededup (Image Deduplication) is available on Github now 🤩
GitHub:
https://lnkd.in/d8bTvf6
Docs:
https://lnkd.in/dDRpNiU
It allows you to find duplicate images (near and exact) with a variety of #hashing methods and #ConvolutionalNeuralNetworks. Anyone who is doing applied Computer Vision knows the pain duplicate images can cause, and even in research datasets this can be an issue, see our Cifar-10 example notebook Example:

https://lnkd.in/ddB97nf

❇️ @AI_Python_en

.

just published my (free) 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV!
Includes step-by-step instructions on:
- Getting Started
- Face Applications
- Object Detection
- OCR
- Embedded/IOT
- and more!
Check it out here:
http://pyimg.co/getstarted
And if you liked it, please do give it a share to spread the word. Thank you!
#Python #Keras #MachineLearning #ArtificialIntelligence #AI

❇️ @AI_Python_EN

New tutorial! Traffic Sign Classification with #Keras and #TensorFlow 2.0

- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python code

http://pyimg.co/5wzc5

#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI #computervision

❇️ @AI_Python_EN

Kick-start your Python Career with 56 amazing Python Open source Projects
#python #programming #technology #project

https://data-flair.training/blogs/python-open-source-projects/

❇️ @AI_Python_EN

How to process images in Python

https://morioh.com/p/f469f74855f6

#Python

❇️ @AI_Python_EN

The Mind at Work: Guido van Rossum on how Python makes thinking in code easier

https://blog.dropbox.com/topics/work-culture/-the-mind-at-work--guido-van-rossum-on-how-python-makes-thinking

#Python

❇️ @AI_Python_EN

Decision trees are extremely fast when it comes to classify unknown records. Watch this video to know how Decision Tree algorithm works, in an easy way - http://bit.ly/2Ggsb9l

#DataScience #MachineLearning #AI #ML #ReinforcementLearning #Analytics #CloudComputing #Python #DeepLearning #BigData #Hadoop