Neural Networks – Basics

Neural Networks

Neural networks have many applications in artificial intelligence, including image and speech recognition, natural language processing, recommendation systems, and autonomous vehicles. They are particularly useful for tasks that involve complex patterns, such as recognizing objects in images or understanding natural language.

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Research methodology for a PhD 

Research methodology for a PhD refers to the systematic and structured approach that a student chooses to conduct their research investigation. It includes the methods, techniques, and tools used to collect, analyze, and interpret data in order to answer the research question or hypothesis. The choice of research methodology depends on the nature of the research problem, the field…

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The LLM Blueprint: How to Architect Intelligence from the Ground Up.

Infographic showing the LLM development lifecycle with a central AI head and steps for data collection, base model training, fine-tuning and alignment, evaluation, and deployment connected in a circular workflow.

Let’s break down what an LLM (Large Language Model) is and then discuss the process of creating a foundational model. This is a complex topic, so I’ll try to be as clear and comprehensive as possible while avoiding overly technical jargon where feasible. What is an LLM? (Large Language Model) Key Architecture: The Transformer Most modern LLMs are based…

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Beyond the Line: How Activation Functions Unlock Complex Learning in Neural Networks

Here are some of the most famous activation functions used in neural networks, along with their advantages and disadvantages: 1. Sigmoid Function: Output: Ranges between 0 and 1 (squashes the input values between 0 and 1). Advantages: Smooth output, making it suitable for modeling probabilities (often used in output layer for binary classification). Well-behaved gradients for backpropagation (a technique…

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