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End to End Big Data Hadoop Project Customer Complaints Analysis buff.ly/3AcqrIr #bigdata #apachespark #datascience #machinelearning #hadoop #programming #programmer #developer #code #codinglife #tech #100DaysOfCode #100daysofcodechallenge #100DaysOfMLCode #Hive
Practice of Robotic Process Automation #RPA bit.ly/3tOm4R3 #Python #datascience #DataAnalytics #DeepLearning #AI #R #Tech #Data #dx #NeuralNetworks #IoT #DEVCommunity #100DaysOfCode #100DaysOfMLCode @antgrasso @pierrepinna @mvollmer1
End to End Big Data Hadoop Project YouTube Data Analysis buff.ly/3ubCrpS #bigdata #apachespark #datascience #machinelearning #hadoop #programming #programmer #developer #code #codinglife #tech #100DaysOfCode #100daysofcodechallenge #100DaysOfMLCode #Hive #apachepig
Building a simple vanilla GAN with PyTorch #neuralnetworks #neuralnetwork #deeplearning #machinelearning #keras #tensorflow #python #pytorch #lightning #ignite #datascience #model #100daysofcode #100daysofmlcode machinecurve.com/index.php/2021…
What can you do when your machine learning model stops improving? There's always a point when you hit the ceiling and the performance of the model stalls. Thread: A couple of tricks to improve your model.
When Programmers write poetry, you get something like this 😂 #DEVCommunity #MachineLearning #Python #IoT #flutter #AI #javascript #100DaysOfMLCode #womenwhocode #RStats #Serverless #CodeNewbie #DataScience #100DaysOfCode
A simple Python trick: Use triple quotes (""") to span strings over multiple lines. It makes up for a much cleaner code. string2 instead of string1 on the attached example.
New Machine Vision model via Prophesee▶️ #Analytics #BigData #AI #MachineLearning #Rstats #Reactjs #IoT #IIoT #ML #Linux #serverless #flutter #javascript #TensorFlow #CloudComputing #Robotics #SelfDrivingCars #SmartHome #AR #mHealth #RPA #100DaysOfCode
If you want to make your first open-source contribution, but don't know how, I made this step-by-step guide. denic.hashnode.dev/make-your-firs…
Don't stress too much about finding "the perfect machine learning model." More often than not, this is a waste of time. Focus on cleaning your data instead. A good model + good data is better than a perfect model + bad data.
One way to get good at machine learning: 1. Learn Python 2. Learn how to use notebooks 3. Get a good book 4. Finish one course 5. Solve many exercises 6. Focus on the analysis 7. Add math as you go 8. Practice with real problems 9. Improve as a developer 10. Stay curious
Info-graphic listing 9 Essential Machine Learning Algorithms to learn. #Python #programming #100DaysOfCode #MachineLearning #100DaysOfMLCode #AI #javascript #womenwhocode #RStats #Serverless #CodeNewbie #DataScience #DEVCommunity
A Collection of some of the best PyTorch courses for beginners to learn PyTorch online. blog.coursesity.com/best-pytorch-t… #Python #programming #100DaysOfCode #MachineLearning #100DaysOfMLCode #AI #javascript #womenwhocode #RStats #Serverless #CodeNewbie #DataScience #DEVCommunity
There are a million things you could do to improve a machine learning model. If you are thinking of hyperparameters, you are right! But there's something even better: Focus on fixing your broken data. Nothing will give you a better return for your time.
Day 5 ML discussion of #100DaysOfCode #100DaysOfMLCode. Difference between overfitting, underfitting and right fit. Hope it helps. #ArtificialIntelligence #AI #DataScience #MachineLearning #CodeNewbie #Python #ML
🔰A Quick Start To Data Quality Monitoring For #MachineLearning! #AI #ML #Python #tech #data #javascript #FemTech #Flutter #webdev #CodeNewbie #React #reactjs #Serverless #coding #Linux #100DaysOfCode #DataScience #100DaysOfMLCode #WomenWhoCode towardsdatascience.com/a-quickstart-g…
Like 100 Pages 👇, MLE book is free: mlebook.com twitter.com/Jeande_d/statu…
This book 👇 is The Hundred Pages Machine Learning. It's that type of book that you can read in a week or less. It's so small but it covers almost anything, from ML concepts, shallow learning algorithms, neural networks, and more. 📗Free to read: themlbook.com/wiki/doku.php
One of my favorite theoretical ML books is Machine Learning Engineering by @burkov The book covers a whole ML workflow, from: ◆ Problem formulation ◆ Data storage, collection, preparation to ◆ Model building, evaluation, deployment, serving, monitoring, and maintenance
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