Numyard_DataScience

The easiest way to have your code available on the #Python path when using virtualenv and pip is to have a buff.ly/3p7OEe5 file and install your project in editable mode when developing Credit: pytest-django plugin documentation buff.ly/38qJqnY #DataScience

If you're learning AI/ML/DL - one of the hardest pieces is knowing what projects to try next! This is a golden link! Over 200 different tutorials across nearly every major branch of Machine Learning!!! buff.ly/2OBX9fk

A great repository containing material that teaches you #Python and important topics like inheritance and debugging. It emphasizes on handling data which makes it a nice starting point for someone jumping into data analysis with Python buff.ly/35aVWWB #DataScience

If you have a widescreen monitor, this new Rstudio 1.4 feature may be relevant to your interests. buff.ly/3p8XsAv #rstats #datascience #MachineLearning

Here's a nice thread by Elvis, on course recommendations to get started with machine learning buff.ly/3msCHgN #machinelearning #datascience

Tokenizers 0.9 is out, and you are going to love it! - Full support for Unigram (SentencePiece) - Numpy arrays as input - Many new components for better customization - Shiny new docs: buff.ly/3leyoWh Check how to create a byte-level Unigram: #deeplearning #nlp

Zero Shot Classification with Huggingface Pipeline: Video Link : buff.ly/36gcFqS #machinelearning #deeplearning #nlp #datascience

Another Python standard library gem: difflib.get_close_matches returns a list of the best “good enough” matches. #python #machinelearning #datascience

This Keras guide on fine-tuning EfficientNets is the one of the best we have seen to date. Includes many practical tips. buff.ly/3bJcruL #deeplearning #machinelearning #datascience

Fun time. Want to generate your daily horoscope? AstroGPT - GPT-2 model fine-tuned on Western zodiac signs Input today's date on: buff.ly/3bKalL0 #nlp #machinelearning #deeplearning #datascience

#fastpages is a backbone of #fastai library that brings advanced #Python constructs like typedispatch, store_attr(), delegates, @patch.. See examples below: buff.ly/3lS4jfN #machinelearning #deeplearning #datascience

Here's a reinforcement learning from human feedback to train language models for summarization. The resulting models produce better summaries than 10x larger models trained only with supervised learning: buff.ly/3bslFey #machinelearning #deeplearning #datascience

If you found qlalchemy for basic DB querying confusing, here's a tiny library, `fastsql` by Jeremy Howard that makes it quite a bit simpler. Here's an example of grabbing data from a DB. buff.ly/2DNXwmo #machinelearning #python #deeplearning #datascience

This is 100% #Julia language code. It trains a dense neural network using the #Keras #Python library. If you replace the first 3 lines with "import tensorflow as tf; from #tensorflow import keras", you can run the exact same code in Python. #machinelearning #datascience

several ways to interactively display results of a calculation: # Two liner >>> q = 3.5 * 1.5 >>> q 5.25 # Second expression >>> q = 3.5 * 1.5; q 5.25 # Walrus >>> (q := 3.5 * 1.5) 5.25 # Invisible >>> q = 3.5 * 1.5 #python #datascience #machinelearning

Ultimate Python study guide for newcomers and professionals alike.. buff.ly/3ifMyF3 #python #machinelearning #datascience

This blog post provides another great example of how to easily build a semantic search engine using Huggingface transformers and Elasticsearch. buff.ly/3ifMyF3 #nlp #deeplearning #machinelearning #datascience

This repo contains a few survey papers related to machine learning. Survey papers are a great way to review current trends in ML and other related areas, and can help you find interesting research topics. buff.ly/3m8BuMw #machinelearning #deeplearning #datascience

E-BERT proposes ideas on how to improve a BERT based language model for e-commerce use cases. The proposed method enhances BERT by modeling product & phrase-level knowledge enabling capabilities buff.ly/3m6rQd4 #machinelearning #deeplearning #nlp #datascience

Here's an amazingly easy way to make custom colormaps: custom_cmap = LinearSegmentedColormap.from_list( "my_cmap", ["red", "black", "white"]) matplotlib docs are the gift that never stops giving. buff.ly/2ZoqTTR #python #machinelearning #datascience