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#neural-networks
Welcome to the DataSavantMaven blog, where the fusion of data engineering, machine learning, and data science opens the road for ground-breaking discoveries. I am eager to offer my knowledge and thoug…
Introduction Most models used in modern-day computer vision tasks like object detection, generative computer vision, object recognition, and so on all have their backbone architecture in convolutional…
Step1: Import Necessary Libraries Numpy: Numpy arrays are very fast and can perform large computations in a very short time. Matplotlib: Used for Visualizations. Tensorflow: This is an open-source lib…
Deep learning is a branch of machine learning and artificial intelligence that uses advanced computations to model the structure and operation of the neural networks in the human brain. In order to automatically extract and learn complicate…
Neural networks are a fascinating and powerful tool for solving complex problems in a wide range of fields, from image recognition to natural language processing. In this article, we’ll provide a brie…
In the previous part of our blog series, we discussed how to initialize a neural network (NN) model with specified layers and hidden units. Now, in this part, we will explore the forward propagation a…
What is a neural network? A neural network is just a mathematical function, which contains some layers. "No Worries we will discuss more about layers in this blog" Let's Consider a simple function tha…
Currently, we find ourselves in the middle of the hype surrounding Artificial Intelligence (AI) and all its buzzwords. It's natural to feel that terms related to AI can be complex and overwhelming to…
Introduction to CNNs and their applications Definition and Purpose of Convolutional Neural Networks (CNNs): Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designe…
Linearity vs non-Linearity Linearity In machine learning, linearity refers to a relationship between two variables where a change in one variable is proportional to the change in the other variable. F…
