1. About Data Analysis Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data...
episensr - Quantitative Bias Analysis for Epidemiologic Data (=simulation of possible effects of different sources of bias) (R package). Machine Learning Tutorials Statistical Inference and Regression Applied Machine Learning in Python Convolutional Neural Networks for Visual Recognition - Stanford CS class...
The next step is to split the data the same way as before: Python >>>x_train,x_test,y_train,y_test=train_test_split(...x,y,test_size=0.4,random_state=0...) Now you have the training and test sets. The training data is contained inx_trainandy_train, while the data for testing...
(bowling_df,schema=bowling_schema) #distributed data cleansing operations to prepare data for analysis using matplotlib clean_batting_spark_df = batting_spark_df\ .filter("Runs!= 'DNB' AND Runs!='sub' AND Runs!='absent' AND Runs!='TDNB'")\ .withColumn('Runs', regexp_replace('Runs',...
A simple example implementation may be written as follows. For more information, check outthe docs. from astropy import units as u import setigen as stg antenna = stg.voltage.Antenna(sample_rate=3e9*u.Hz, fch1=6000e6*u.Hz, ascending=True, num_pols=1) antenna.x.add_noise(v_mean=0,...
Convolutional neural networks (CNNs) outperform traditional approaches in terms of image quality metrics such as peak signal to noise ratio (PSNR) and structural similarity (SSIM). However, generative adversarial networks (GANs) offer a competitive advantage by being able to mitigate the issue of a...
Chapter 6: Signal & Noise Chapter 7: Image Processing & Analysis Chapter 8: Mathematics Chapter 9: Simulations Chapter 10: Plotting with Seaborn Chapter 11: Nuclear Magnetic Resonance with NMRglue Chapter 12: Machine Learning using Scikit-Learn ...