1 line to ELECTRA Word Embeddings with NLU in Python

Original Source Here 5. Prepare data for T-SNE We prepare the data for the T-SNE algorithm by collecting them in a matrix for TSNE import numpy as npmat = np.matrix([x for x in predictions.electra_embeddings]) 6. Fit T-SNE Finally, we fit the T-SNE algorithm and get our 2-Dimensional representation of our Electra Word Embeddings from sklearn.manifoldContinue reading “1 line to ELECTRA Word Embeddings with NLU in Python”

1 line to COVIDBERT Word Embeddings with NLU in Python

Original Source Here 1 line to COVIDBERT Word Embeddings with NLU in Python Including Part of Speech, Named Entity Recognition, Emotion Classification in the same line! With Bonus t-SNE plots! 0. Introduction 0.1 What is NLU? John Snow Labs NLU library gives you 1000+ NLP models and 100+ Word Embeddings in 300+ languages and infiniteContinue reading “1 line to COVIDBERT Word Embeddings with NLU in Python”

1 line to ALBERT Word Embeddings with NLU in Python

Original Source Here 5. Prepare data for T-SNE We prepare the data for the T-SNE algorithm by collecting them in a matrix for TSNE import numpy as npmat = np.matrix([x for x in predictions.albert_embeddings]) 6. Fit T-SNE Finally, we fit the T-SNE algorithm and get our 2-Dimensional representation of our Albert Word Embeddings from sklearn.manifoldContinue reading “1 line to ALBERT Word Embeddings with NLU in Python”

1 line to GLOVE Word Embeddings with NLU in Python

Original Source Here 5. Prepare data for T-SNE We prepare the data for the T-SNE algorithm by collecting them in a matrix for TSNE import numpy as npmat = np.matrix([x for x in predictions.glove_embeddings]) 6. Fit T-SNE Finally, we fit the T-SNE algorithm and get our 2-Dimensional representation of our Glove Word Embeddings from sklearn.manifoldContinue reading “1 line to GLOVE Word Embeddings with NLU in Python”

A Critical Appraisal of Deep Learning

https://cdn-images-1.medium.com/max/2600/0*bGxYJIwGwceQEJf- Original Source Here A Critical Appraisal of Deep Learning The field has seen some strong progress, but the more we know, the more we realize we know nothing. Photo by Derek Owens on Unsplash Introduction Everyone and their grandparents are talking about it: Artificial Intelligence, Deep Learning (DL), Machine Learning, Robotics, etc… Sometimes allContinue reading “A Critical Appraisal of Deep Learning”

The Top Technology Trends and Their Impact on Data Science, Machine Learning and AI

Original Source Here The last block is called resilient delivery. Resilience means “the ability of a substance to return to its usual shape after being bent, stretched, or pressed.” While companies focused in the past years on optimized, efficient operations, COVID-19, and the current recession hit them hard in their fragile processes. So, technology-driven resilienceContinue reading “The Top Technology Trends and Their Impact on Data Science, Machine Learning and AI”

Introduction to Recursion and Merge Sort

https://cdn-images-1.medium.com/max/2600/0*pan7GXYKzBvCozA1 Original Source Here Let us use our algorithm to see if it works: a = [82, 10, 89, 62, 77, 62, 63, 73, 95, 73, 74, 53, 14, 18, 41]mergesort(a)# out: [10, 14, 18, 41, 53, 62, 62, 63, 73, 73, 74, 77, 82, 89, 95] Great! This is all you need to understandContinue reading “Introduction to Recursion and Merge Sort”

Data Science Career Path Rankings

https://cdn-images-1.medium.com/max/2600/0*gc1XNQK5oKwqmJYt Original Source Here Data Science Career Path Rankings Navigating Your Career In Data Science Photo by Tim Graf on Unsplash You’ve tirelessly iterated over your CV, you’ve worked on multiple projects for your portfolio, you’ve sat in countless interviews, and one day you receive a call from an unrecognized number. You pick up —Continue reading “Data Science Career Path Rankings”

All About Imbalanced Machine Learning Classifiers

https://cdn-images-1.medium.com/max/2600/0*ge58xkRUjwLIxvmN Original Source Here MACHINE LEARNING. PYTHON. All About Imbalanced Machine Learning Classifiers A Comprehensive Guide to Handling Imbalanced Datasets INTRODUCTION One of the mistakes I made as a rookie data scientist was placing heightened importance on the accuracy metric. Now, this is not to dismiss the importance of accuracy as a measure of machineContinue reading “All About Imbalanced Machine Learning Classifiers”

A Machine Learning Roadmap to Naive Bayes

Original Source Here A Machine Learning Roadmap to Naive Bayes Naive Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we shall be understanding the Naive Bayes algorithm and its essential concepts so that there is no room for doubts inContinue reading “A Machine Learning Roadmap to Naive Bayes”