Category: Deep Learning

LLMs in 2026: ChatGPT, Gemini, DeepSeek and Local Models


The transition from late 2025 to early 2026 highlights a significant shift in AI, emphasizing specialized reasoning over purely larger models. This period marks the rise of complex reasoning agents through innovations from major players like OpenAI, Anthropic, and Google, alongside the growing use of open-source models. Key developments include OpenAI’s GPT-5.2 and Anthropic’s Claude Opus 4.5, addressing distinct user needs in logic and engineering respectively. Advances in model architecture, including Mixture-of-Experts, have facilitated local inference capabilities, prompting changes in pricing and market strategies. The landscape now demands multi-model workflows due to competitive dynamics.… Read More LLMs in 2026: ChatGPT, Gemini, DeepSeek and Local Models

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Machine Learning and AI for Developing Business Strategies and Real-world Use Cases


Enterprises and institutions seek to enhance efficiency and outcomes through machine learning (ML). This paper explores ML’s application in optimization, emphasizing problem definition, data accumulation, feature engineering, model selection, training, evaluation, optimization, deployment, and monitoring. ML provides data-driven insights revolutionizing decision-making and operations, driving innovation and adaptation to market dynamics.… Read More Machine Learning and AI for Developing Business Strategies and Real-world Use Cases

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Unsupervised Learning: Exploring the Enigmas of Unlabeled Data


In the realm of machine learning, there are two main branches: supervised learning and unsupervised learning. While supervised learning relies on labelled data for model training, unsupervised learning takes on the challenge of working with unlabeled data, and autonomously uncovering patterns, relationships, and structures within the data.… Read More Unsupervised Learning: Exploring the Enigmas of Unlabeled Data

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Machine Learning and Data Science Engineering


Machine learning and data science engineering are two rapidly growing fields that are revolutionizing the way we analyze and make decisions using data. With the increasing amount of data being generated by businesses, governments, and individuals, there is a growing demand for professionals with the skills and knowledge to extract meaningful insights from this data.… Read More Machine Learning and Data Science Engineering

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Deep Learning Applications with Python, Keras and TensorFlow


Artificial neural networks (ANNs) is one of the most commonly used machine learning methods due to their outstanding success with accurately operating on various data types, and their high prediction performance. Moreover, deep learning, the most popular machine learning technique in the recent years, is also a type of ANN which consists of multiple layers… Read More Deep Learning Applications with Python, Keras and TensorFlow

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