2022-06-10
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Minimal PyTorch LSTM example for regression and classification tasks

10 mins read The Idea Behind RNNs Recurrent neural networks in general maintain state information about data previously passed through the network. This […]
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2022-06-06
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Understand Q-Learning in Reinforcement Learning with a numerical example and Python implementation

14 mins read This tutorial introduces the concept of Q-learning through a simple but comprehensive numerical example.  The example describes an agent which […]
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2022-06-03
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A complete guide on feature selection techniques with Python code

33 mins read Considering you are working on high-dimensional data that’s coming from IoT sensors or healthcare with hundreds to thousands of features, […]
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2022-05-30
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A tutorial on Scikit-Learn Pipeline, ColumnTransformer, and FeatureUnion

20 mins read These three powerful tools are must-know for anyone who wants to master using sklearn. It’s, therefore, crucial to learn how to […]
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2022-05-28
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How to interpret logistic regression coefficients?

15 mins read Logistic Regression is a fairly simple yet powerful Machine Learning model that can be applied to various use cases. It’s […]
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2022-05-28
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Understanding interaction effects in regression analysis

22 mins read In regression, an interaction effect exists when the effect of an independent variable on a dependent variable changes, depending on […]
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2022-05-26
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Understanding Ordinal and One-Hot Encodings for categorical features

21 mins read Machine learning models require all input and output variables to be numeric. This means that if your data contains categorical […]
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2022-05-26
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When should we drop the first one-hot encoded column?

10 mins read Many machine learning models demand that categorical features are converted to a format they can comprehend via a widely used […]
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2022-05-26
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Alternatives for One-Hot Encoding of Categorical Variables

6 mins read One-hot encoding, otherwise known as dummy variables, is a method of converting categorical variables into several binary columns, where a […]
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2022-05-26
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Common mistakes to avoid as a Machine Learning Engineer

5 mins read In machine learning, there are many ways to build a product or solution and each way assumes something different. Many […]
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2022-05-25
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What are skip connections in deep learning?

17 mins read Nowadays, there is an infinite number of applications that someone can do with Deep Learning. However, in order to understand […]
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2022-05-24
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A guide on Maximum likelihood and Bayesian inference for parameter estimation

28 mins read Introduction In this post, I’ll explain what the maximum likelihood and Bayesian inference methods for parameter estimation are and go […]
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2022-05-19
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Understanding the basics of Bayesian Inference with Python Code

10 mins read Why did someone have to invent the Bayesian Inference? In one sentence: to update the probability as we gather more data. The […]
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2022-05-10
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Understanding GloVe embedding with Tensorflow implementation

9 mins read In this article, you will learn about GloVe, a very powerful word vector learning technique. This article will focus on […]
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2022-05-09
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Understanding Word2vec embedding with Tensorflow implementation

15 mins read This article is going to be about Word2vec algorithms. Word2vec algorithms output word vectors. Word vectors, underpin many of the […]
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2022-05-08
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Encoding categorical features using the category_encoders package

11 mins read There are loads of different ways to convert categorical variables into numeric features so they can be used within machine […]
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2022-05-04
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Understand different feature scaling techniques with Python code

19 mins read In many machine learning algorithms, to bring all features in the same standing, we need to do scaling so that […]
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2022-04-27
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A tutorial on TensorFlow 2 and Keras: From basics to deep neural networks

49 mins read Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep […]
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2022-04-27
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Understanding and interpreting Residuals Plot for linear regression

27 mins read Interpreting Residual Plots to Improve Your Regression When you run a regression, calculating and plotting residuals help you understand and improve your […]
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2022-04-24
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Implementing Transformers step-by-step in PyTorch from scratch

14 mins read Doing away with clunky for-loops, the transformer instead finds a way to allow whole sentences to simultaneously enter the network […]
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