Category Archives: Computing

Original Source Here Docker crash course for Machine Learning Engineers and Data Scientists — Part 1 As a Senior Solutions architect working with Databricks, I work with some of our biggest enterprise customers and provide them with best practices on scaling ML model deployments. Interestingly a common scenario for deploying ML models is as aContinue reading “Docker crash course for Machine Learning Engineers and Data Scientists — Part 1”

https://miro.medium.com/max/1200/0*tOAHh4bLj3ZRqX9x Original Source Here SVM with Scikit-Learn: What You Should Know Why LinearSVC and SVC with a linear kernel are not the same functions? To create a linear SVM model in scikit-learn, there are two functions from the same module svm: SVC and LinearSVC . Since we want to create an SVM model with aContinue reading “SVM with Scikit-Learn: What You Should Know”

Original Source Here AlphaFold: What the heck is Protein Folding and Why should we care? Literally, that’s the question I asked myself when I heard about AlphaFold. 😒 Last year, Google DeepMind released an AI model named AlphaFold that can predict the structure of a protein given the protein sequence. And this year (July 22,Continue reading “AlphaFold: What the heck is Protein Folding and Why should we care?”

https://miro.medium.com/max/1200/0*XG192viu0eboZPqj Original Source Here import numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import load_iris, load_bostonfrom sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressorfrom nonconformist.icp import IcpClassifier, IcpRegressorfrom nonconformist.nc import ClassifierNc, MarginErrFunc, ClassifierAdapter, RegressorNc, AbsErrorErrFuncfrom sklearn.model_selection import train_test_splitdef regression_calibration_curve(estimator, X, y, alphas=np.linspace(0.1,1,10, endpoint=True)): errors = [] interval_sizes = [] for a in alphas: pred = estimator.predict(X_test, significance=a) interval_sizes.append(np.mean([y-x for x,Continue reading “How to Add Uncertainty Estimation to your Models with Conformal Prediction”

Original Source Here Forecasting News Coverage An introduction to datafloat.ai Photo by Good Good Good on Unsplash Forecasting is a journey. We learned a lot along the way. We made mistakes (we still do). In our blog we try to document those learnings as they happen. In Predicto, it’s important to iterate and experiment withContinue reading “Forecasting News Coverage”

Original Source Here Engaging ML Tracking and Visualization : Sit Back and Relax Chapter 1 — W&B Learn Something New: The Golden Pheasant are popularly known as the ‘Chinese Pheasant’ as they are native to Western and Central China, living primarily in the mountainous forests. From The New York Public Library — https://on.nypl.org/3zwIhFJ The worldContinue reading “Engaging ML Tracking and Visualization : Sit Back and Relax”

Original Source Here Impacts of Cybercrimes on Indian Youth-A critical Analysis of Cybercrime Policies. Introduction Before the implementation of cybersecurity rules and regulations, every administration kept in mind the results that might be feasible in case of a decline in the cybercrime rate. Therefore, the impacts of cybercrimes are more catastrophic than ordinary cyber criminalitiesContinue reading “Impacts of Cybercrimes on Indian Youth-A critical Analysis of Cybercrime Policies.”

Original Source Here Complete Machine Learning Breakdown Machine learning evolved from LEFT to RIGHT as you can see from the diagram given above. Firstly, researchers started off with Supervised Learning, this is where the machine is made to learning with some sort of supervision, the best example would be the house price prediction problem statement,Continue reading “Complete Machine Learning Breakdown”

https://miro.medium.com/max/1200/0*sfatXQPwIQWMExmw Original Source Here 7. Merging the values: ### Creating a dataframeimport pandas as pddataset1 = {‘Fruits’: [“Apple”, “Mango”, “Grapes”, “Strawberry”, “Oranges”], ‘Supply’: [30, 15, 10, 25, 20]}dataset2 = {‘Fruits’: [“Melons”, “Pear”], ‘Supply’: [10, 20]}# Create DataFramedf1 = pd.DataFrame(dataset1)df2 = pd.DataFrame(dataset2)# Print the output.df1.merge(df2, how = “outer”) Image By Author Assume that we have twoContinue reading “14 Pandas Operations That Every Data Scientist Must Know!”

Original Source Here دستیاران دیجیتال که خود را با نیازهای هر فروشگاه تطبیق می‌دهند پاندمی کرونا به ما آموخت که تقریباً همه شرکت‌ها در نهایت مجبور به فروش محصولات خود در اینترنت هستند. ربات‌ها فناوری هستند که تجارت الکترونیک را تسهیل می‌کنند. این ربات‌ها می‌توانند دستیاران دیجیتال باشند که به سوالات مشتریان در مورد کالاهاییContinue reading “دستیاران دیجیتال که خود را با نیازهای هر فروشگاه تطبیق می‌دهند”