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Getting into Deep Learning? Here are 5 Things you Should Absolutely Know

Analytics Vidhya

Starting your Deep Learning Career? Deep learning can be a complex and daunting field for newcomers. The post Getting into Deep Learning? Concepts like hidden layers, convolutional neural networks, backpropagation. Here are 5 Things you Should Absolutely Know appeared first on Analytics Vidhya.

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Optimization Essentials for Machine Learning

Analytics Vidhya

Overview There are 4 mathematical pre-requisite (or let’s call them “essentials”) for Data Science/Machine Learning/Deep Learning, namely: Probability & Statistics Linear Algebra Multivariate Calculus Convex Optimization Introduction In this article, we are going to discuss the following questions: Why should I bother about Optimization (..)

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Creating a Simple Z-test Calculator using Streamlit

Analytics Vidhya

Statistics plays an important role in the domain of Data Science. It is a significant step in the process of decision making, powered by Machine Learning or Deep Learning algorithms. One of the popular statistical processes is Hypothesis Testing having vast usability, not […].

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KL Divergence: The Information Theory Metric that Revolutionized Machine Learning

Analytics Vidhya

Introduction Few concepts in mathematics and information theory have profoundly impacted modern machine learning and artificial intelligence, such as the Kullback-Leibler (KL) divergence. This powerful metric, called relative entropy or information gain, has become indispensable in various fields, from statistical inference to deep learning.

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Applying Occam’s razor to Deep Learning

KDnuggets

Finding a deep learning model to perform well is an exciting feat. A simple complexity measure based on the statistical physics concept of Cascading Periodic Spectral Ergodicity (cPSE) can help us be computationally efficient by considering the least complex during model selection.

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Data Science Interview Series: Part-1

Analytics Vidhya

Introduction Data science interviews consist of questions from statistics and probability, Linear Algebra, Vector, Calculus, Machine Learning/Deep learning mathematics, Python, OOPs concepts, and Numpy/Tensor operations. This article was published as a part of the Data Science Blogathon.

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Introduction to Linear Model for Optimization

Analytics Vidhya

Optimization aims to reduce training errors, and Deep Learning Optimization is concerned with finding a suitable model. Another goal of optimization in deep learning is to minimize generalization errors. In this article, we will […].