Convert array of indices to one-hot encoded array in NumPy Introduction One-hot encoding is a common technique used in machine learning to represent categorical data. In this technique, each category…
Advantages of Artificial Neural Networks over Support Vector Machines Advantages of Artificial Neural Networks over Support Vector Machines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) are both powerful…
Understanding Logits in TensorFlow What are Logits in TensorFlow? In TensorFlow, “logits” represent the raw, unscaled output of a neural network’s final layer before applying an activation function. They essentially…
How Does the Google “Did You Mean?” Algorithm Work? Introduction Google’s “Did You Mean?” feature is a ubiquitous part of our search experience. It suggests alternative search queries when it…
Epoch vs Iteration in Neural Network Training Epoch vs Iteration: Understanding the Fundamentals of Neural Network Training Training a neural network involves feeding it data and adjusting its internal parameters…
A Simple Explanation of Naive Bayes Classification What is Naive Bayes Classification? Naive Bayes Classification is a probabilistic machine learning algorithm used for classification tasks. It’s based on Bayes’ theorem…
What is the Difference Between a Generative and a Discriminative Algorithm? In the realm of machine learning, algorithms are broadly classified into two categories: generative and discriminative. While both aim…
What are Logits? Understanding Logits In machine learning, especially in the context of neural networks, “logits” represent the raw, unnormalized output of a neural network before applying any activation function.…
The Role of Bias in Neural Networks What is the Role of Bias in Neural Networks? Bias, a crucial component of neural networks, plays a pivotal role in their ability…
Social Media vs. SEO: It’s Not an Either/Or, It’s a Powerful And While social media marketing and SEO are both digital marketing powerhouses, declaring one definitively “better” than the other…
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