Tag: Machine Learning

ChatGPT, a debated AI force, employs an innovative Attention Mechanism to explore vast internet knowledge. Despite creative versatility, it lacks post-2021 data precision and common sense. As we anticipate Artificial General Intelligence (AGI) by 2050, job displacement concerns linger. While ChatGPT’s impact is gradual due to its common sense limitations, adapting to evolving technology remains crucial. A collaborative future beckons, where honing new skills redefines our relationship with advancing machines.

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Artificial Intelligence harnesses algorithms like Breadth First Search (BFS) and Depth First Search (DFS) for effective problem-solving. Before delving into these techniques, it’s vital to understand the core of a search algorithm. From ancient foragers to modern navigators, our pursuit of solutions has evolved. Illustrated through Lisa’s quest for a rare lipstick shade, BFS shows organized yet potentially redundant traits, while the strategic DFS proves more efficient. Both algorithms face challenges in larger search spaces, paving the way for future exploration into heuristic solutions. Connect for further insights.

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pmf

Probability, a high-school math staple, often gathers rust in our memories. In this blog, we refresh its concepts through a machine learning lens, delving into Probability Mass Function (PMF). By the blog’s end, readers gain insights into probability, distribution, PMF’s expectations, and variance—crucial aspects in machine learning. The code snippet illustrates PMF for a biased coin toss, emphasizing its role in predicting outcomes. Bridging probability theory and machine learning, the blog fosters a deeper understanding of these essential concepts.

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SVM, a potent algorithm championed by Vladimir N. Vapnik, triumphed in image classification after being overlooked for three decades. This supervised machine learning tool classifies data points with hyperplanes, excelling in both binary and multilinear classification. SVM’s quest for an optimal hyperplane involves maximizing margin, achieved through Lagrange Multipliers and the Kernel Trick. Though not the primary choice for modern image classification, SVM proves effective for datasets with fewer parameters, showcasing that machine learning at its core is deeply intertwined with mathematics.

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This blog offers a hands-on exploration of Word2Vec, unraveling its purpose, functionality, and practical implementation. Vital in Natural Language Processing, Word2Vec excels in contextual word vectorization. Unlike simplistic approaches, it positions words with akin meanings closer in vector space. The blog provides a succinct yet comprehensive overview, introducing the Skip-gram model and culminating in a simplified Python-based implementation using Gensim. Essential for those seeking a swift entry into impactful word representation.

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When we do the training we divide the data into two sets, train and test. this is called a hold-out set where we keep some data outside the training. This way we prevent the model from learning the test dataset. However, this method is not useful everywhere. Choosing the right cross-validation is very important as the different datasets may require different cross-validations.

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