Deep Learning: The Next Step in Machine Learning
In our previous blog, "Machine Learning 101: A Beginner’s Handbook," we laid the groundwork by explaining the fundamental concepts of
Driving into the AI Future: The Integration of AI in Automobiles
In today's rapidly evolving technological landscape, the automotive industry stands at the forefront of innovation. From electric vehicles to self-driving
Self-Supervised Learning — A Comprehensive Introduction
Notes: For more articles on Generative AI, LLMs, RAG etc, one can visit the “Generative AI Series.” For such library of
New Tech ALERT: Introducing AutoGen, Revolutionizing AI Collaboration
AutoGen, a ground-breaking project by Microsoft, introduces a paradigm shift in AI collaboration. This innovative framework allows the creation and
Adam: Efficient Deep Learning Optimization
Adam (Adaptive Moment Estimation) is an optimization algorithm commonly used for training machine learning models, particularly deep neural networks. It
Accelerate Convergence: Mini-batch Momentum in Deep Learning
Imagine you're climbing a hill, and you want to find the quickest way to reach the top. There are different
EfficientDL: Mini-batch Gradient Descent Explained
Mini-batch Gradient Descent is a compromise between Batch Gradient Descent (BGD) and Stochastic Gradient Descent (SGD). It involves updating the
Efficient Opti: Mastering Stochastic Gradient Descent
Stochastic Gradient Descent (SGD) is a variant of the Gradient Descent optimization algorithm. While regular Gradient Descent computes the gradient
OptiLearn: Mastering Gradient Descent
Gradient Descent is a fundamental optimization algorithm widely used in training deep learning models. It's a process that helps the
Mastering Activation Functions: Unleashing Neural Power
Activation Function: At the heart of artificial neural networks, an activation function plays a crucial role in introducing non-linearity to the