Machine Learning Expedition

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Machine Learning Expedition

Application of Machine Learning in Healthcare

Machine Learning (ML) in Healthcare: Transforming the Future of Patient Care Over the last ten years, the healthcare sector has witnessed a remarkable surge in the application of machine learning (ML). This technology has laid a solid foundation for enhancing patient care, reducing the strain on healthcare resources, streamlining processes,

Using Machine Learning Models for Pricing

Pricing is a critical element of any business, impacting profitability, customer acquisition, and market share. Setting the right prices for products and services requires understanding customer demand, production costs, competitor pricing, and market dynamics. Pricing too high can reduce sales volume, while pricing too low leaves money on the table.

Scalable Machine Learning Models for Enterprise-Level Data

Introduction In the rapidly evolving landscape of big data, scalable machine learning models have become a cornerstone for enterprises. The journey from the inception of machine learning to its current pivotal role in handling massive datasets reflects a remarkable evolution. A compelling infographic here illustrates how data volumes have skyrocketed

Kernel Methods for Machine Learning

In the field of machine learning, a kernel serves as a mathematical function tasked with transforming pairs of input vectors into a higher-dimensional space. This elevated dimensionality enables the computation of similarity between the vectors. In the realm of natural language processing (NLP), kernel methods find widespread application in tasks

TabNet: Attentive Interpretable Tabular Learning

Introduction TabNet is a deep learning architecture for tabular data that uses sequential attention to choose which features to reason from at each decision step. This enables interpretability and more efficient learning, as the learning capacity is used for the most salient features. TabNet has been shown to outperform other

Automatic Differentiation in PyTorch

Automatic Differentiation in PyTorch

Introduction Calculating gradients manually is tedious and error-prone. Autodiff allows us to automatically compute gradients of computations defined in a programming language like Python. PyTorch uses reverse mode autodiff to efficiently calculate gradients. It records operations performed on tensors to build up a computational graph, and then applies chain rule

Sequence Classification Using RNNs with FastAI

Sequence Classification Using RNNs with FastAI

Implementing an RNN for sequence classification in Python using the FastAI library