10 Cheat Sheets for Neural Network, Data Analytics, and ML – Analytics Insight



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April 14, 2022

Data analytics

Clean sheets are always necessary for a deep understanding of any concept in 2022

Cutting-edge technologies such as artificial intelligence, neural network, data analytics, and machine learning (ML) are thriving in the global tech market. There are so many minute details to remember for AI, data, and ML professionals that they need cheat sheets for the deeper understanding. Yes, it is overwhelming to get a grip on these technologies within a short period of time. Datasets and machinery concepts are complicated with more advanced mechanisms. ML cheat sheets, data analytics cheat sheets, and neural network cheat sheets are necessary to look out for help to become successful in this highly competitive market. Thus, let’s explore some of the top ten cheat sheets for neural networks, data analytics, and ML to work on in 2022.

Top cheat sheets for the neural network in 2022
Basic terms to understand

There are multiple different basic terms essential to learning to have a deep understanding of the neural network. This cheat sheet includes terms like perceptron, radial basis network, recurrent neural network, autoencoder, Markov chain, deep convolutional network, deep network, generative adversarial network, extreme learning machine, deep residual network, and many more.

Different layers

It is necessary to look out for different layers present in a neural network. The clean sheet for neural network consists of three different layers that can help memorize the minute details of neural networks. This includes an input layer, a hidden layer, and an output layer. Inputs are deposited in the model through the input layer. Hidden layers process these inputs, whereas the processed data is available at the output layer.

Graphical representations

Neural network graphical representations are important to be in the clean sheet including topics such as predicting protein interface, modelling physics systems, non-structural data, and many more. This helps to remember information efficiently and effectively.

Multiple important formulae with concepts

The cheat sheet must consist of multiple different formulae with important concepts such as linear vector spaces, linear independence, Gram Schimdt Orthogonalization, vector expansions, reciprocal basis vectors, Hebb’s postulate, perceptron architecture, general minimization algorithm, stable learning rate, ADALINE, backpropagation algorithm, associative learning rules, and many more.

Top cheat sheets for data analytics in 2022
Importing

The cheat sheet for data analytics must consist of useful and crucial imports that data professionals need to know at all times. This may include importing Pandas, Matplotlib, checking and monitoring data type, and plotting parallel coordinates.

Basic information for data professionals

Data analytics cheat sheet must include the basic information to gain a deeper understanding without any doubt in a workplace. This part of the cheat sheet consists of CSV, column names, column data type, listing of the dataset, column data type manipulation, and many more.

Plotting concepts

It is important for data professionals to know all kinds of plotting concepts for effective data management. Plotting for data analytics include line graphs, boxplots, histogram, parallel, coordinates, and many more graphical representations for better insights into a company.

Understanding statistics

Data professionals need to have a strong understanding of statistics and probabilities to deal with vast datasets. There are multiple different functions and mathematical calculations to work with data to drive meaningful insights. There are several types of statistical analysis and terms such as binomial logistic regression with categorical predictors, multinomial logistic regression, ANCOVA, MANOVA, multiple linear regression, simple linear regression, and many more.

Top cheat sheets for ML in 2022
Classification metrics

Cheat sheets for ML consist of classification metrics to track, monitor, and assess machine learning or ML model performances efficiently and effectively with the main metrics. Classification metrics include concepts such as confusion matrix, main metrics like accuracy, precision, recall sensitivity, specificity, F1 score, ROC, and AUC. Regression metrics offer basic metrics and coefficient of determination and many more.

Model selection

Machine learning professionals are needed to include model selection in one of the cheat sheets for ML. It includes minute details and parts of concepts such as vocabulary, cross-validation, regularization, diagnostics, and so on with training sets, validation sets, and testing sets.

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