Best Deep Learning Courses:
https://mltut.com/best-deep-learning-courses-on-coursera/
https://mltut.com/best-deep-learning-courses-on-coursera/
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras
https://t.iss.one/DataScienceM
π3π₯2β€1
This media is not supported in your browser
VIEW IN TELEGRAM
Create pivot tables in your Jupyter Notebook:
Here's the link to the #GitHub repo and documentation:
https://pivottable.js.org/examples/
Here's the link to the #GitHub repo and documentation:
https://pivottable.js.org/examples/
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras
https://t.iss.one/DataScienceM
π6
20x faster KMeans with Faiss!!
#KMeans uses a slow, exhaustive search to find the nearest centroids.
#Faiss uses "Inverted Index"βan optimized data structure to store and index data points for approximate neighbor search.
#KMeans uses a slow, exhaustive search to find the nearest centroids.
#Faiss uses "Inverted Index"βan optimized data structure to store and index data points for approximate neighbor search.
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras
https://t.iss.one/DataScienceM
π6β€2π₯1
Forwarded from Python | Machine Learning | Coding | R
Machine Learning Glossary
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.iss.one/CodeProgrammer
π6
Forwarded from Python | Machine Learning | Coding | R
Media is too big
VIEW IN TELEGRAM
The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.
The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.
The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available
.
All slides, #code and additional materials can be found at the link provided.
π Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.iss.one/CodeProgrammerβ
Please open Telegram to view this post
VIEW IN TELEGRAM
π10
Forwarded from Python | Machine Learning | Coding | R
Numpy @CodeProgrammer.pdf
813.2 KB
π¨π»βπ» For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.iss.one/CodeProgrammerβ
Please open Telegram to view this post
VIEW IN TELEGRAM
π9π₯4β€2
This real-world project tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.
Link: https://realpython.com/practical-prompt-engineering/
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://t.iss.one/CodeProgrammerβ
Please open Telegram to view this post
VIEW IN TELEGRAM
π6
Forwarded from Python | Machine Learning | Coding | R
Keras Cheat Sheet: Neural Networks in Python
#keras #cheatsheet #python #library #programming #guide
https://t.iss.one/CodeProgrammer
#keras #cheatsheet #python #library #programming #guide
https://t.iss.one/CodeProgrammer
β€1π1
Forwarded from Python | Machine Learning | Coding | R
Top_100_Machine_Learning_Interview_Questions_Answers_Cheatshee.pdf
5.8 MB
Top 100 Machine Learning Interview Questions & Answers Cheatsheet
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #Rο»Ώ
https://t.iss.one/CodeProgrammerβ
Please open Telegram to view this post
VIEW IN TELEGRAM
π7β€2
Forwarded from Python | Machine Learning | Coding | R
Machine Learning from Scratch by Danny Friedman
This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practicesβsuch as feature engineering or balancing response variablesβor discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
π Link: https://dafriedman97.github.io/mlbook/content/introduction.html
This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practicesβsuch as feature engineering or balancing response variablesβor discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #R
https://t.iss.one/CodeProgrammerβ
Please open Telegram to view this post
VIEW IN TELEGRAM
π10