“Proof by example” and “slothfull induction” can “prove” to be very dangerous.

Hasty generalization has always plagued the scientific realms for centuries. Often when the stakes are high, a rational argument for justification becomes a crucial requirement to minimize the potential loss that a fallacy could cause. These forms of sloppy approaches are observed very frequently in the field of *sampling.*

*“A minimum of 30 observations is sufficient to conduct significant statistics.”*

This is open to many interpretations of which the most fallible one is that the sample size of 30 is enough to trust your confidence interval. Sampling is a very crucial aspect of experimental analysis and it's always fair to say that the fate of the entire population heavily depends upon the probed sample set, especially when the population parameters are unknown. So in this light, the choice of sampling scheme and the sample size should be corroborated strongly. For this there is no thumb rule yet, and which will never be, considering the chaotic nature of our universe. However, there is certain art or an element of thinking to it while dealing with such questions of sampling. For example, if the population is non-seasonal and clustered then systematic sampling using cluster sampling or stratified sampling may prove to be helpful. With this, the size of the sample is another very crucial question for the experimentalist or statisticians. Often experiments are expensive and one needs to assert the optimum number of observations to conduct reasonably significant statistical analysis. Coming to our popular belief about the number **30**, first, it’s important to understand why it's 30 and then we should be able to appreciate the fact that it's not a thumb rule and could produce fallacious conclusions. …

If you have ever delved into the world of data science, then it will not be an absurdity for me to assume that by now you have certainly encountered the term ** Neural Networks** somewhere, at some point in your journey towards probing Machine Learning and Artificial Intelligence or data science in general.

Applications of machine learning (ML) are now almost an integral part of our everyday life. From a speech-recognition based virtual assistant in our smartphones to super-intelligent automated drones, ML and artificial intelligence (AI) is revolutionizing the dynamics of human-machine interactions. AI algorithms, especially the convolution neural networks (CNN) have made computer vision extremely powerful than ever. While the applications of it are breathtakingly awesome, it could be very intimidating to build one’s own CNN model, especially for a non-programmer or a beginner in data science, in general. As an R lover, it was not difficult for me to assert that it gets even more enigmatic for a novice R programmer. The plausible reason for this imbalance could be that the standard neural network and ML libraries (like Keras and Tensorflow) are primarily compatible with Python and naturally gravitate the masses to roll with Python itself, leading to a severe lack of novice’s guide and documentation to facilitate the implementation of these sophisticated frameworks in R. Nevertheless, APIs of Keras and Tensorflow is now available on CRAN. Herein, we are going to make a CNN based vanilla image-classification model using Keras and Tensorflow framework in R. With this article, my goal is to enable you to conceptualize and build your own CNN models in R using Keras and, sequentially help to boost your confidence through hands-on coding to build even more complex models in the future using this profound API. Apart from the scripting of the model, I will also try as much to concisely elaborate on the necessary components while plunging the hardcore underlying mathematics. …

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