IDE: How to connect GitHub with Visual Studio Code ?

(AI #1) Deep Learning and Neural Networks

I was curious to learn Artificial Intelligence and thinking what is the best place to start learning, and then realized that Deep Learning and Neural Networks is the heart of AI. Hence started diving into AI from this point. Starting from today, I will write continuous blogs on AI, especially Gen AI & Agentic AI. Incase if you are interested on above topics then please watch out this space. What is Artificial Intelligence, Machine Learning & Deep Learning ? AI can be described as the effort to automate intellectual tasks normally performed by Humans. Is this really possible ? For example, when we see an image with our eyes, we will identify it within a fraction of milliseconds. Isn't it ? For a computer, is it possible to do the same within same time limit ? That's the power we are talking about. To be honest, things seems to be far advanced than we actually thing about AI. BTW, starting from this blog, it is not just a technical journal, we talk about internals here. ...

(AI #3) Deep Learning Foundations - Activation & Loss Functions, Gradient Descent algorithms & Optimization techniques

It is extremely important to have a deep knowledge while designing a machine learning model, otherwise we will end up creating ML models which are of no use. We have to have a clear understanding on certain techniques to confidently build a ML model, train it using "training data", finalize the model and to deploy it in production. So far, from blog #1, #2, we have seen about the fundamentals of Deep Learning and Neural Network, architecture of a Neural Network, internal layers and components etc. Providing the links of Blogs #1 , #2 below for quick reference. Deep Learning & Neural Networks : https://arunsdatasphere.blogspot.com/2026/01/deep-learning-and-neural-networks.html Building a real world neural network: A practical usecase explained : https://arunsdatasphere.blogspot.com/2026/01/building-real-world-neural-network.html Now let's dive through below concepts/criteria to help gaining confidence on building your ML model: Activation Functions (Forward Propaga...

(AI #2) Building a Real-World Neural Network: A Practical Use Case Explained

This blog will explain a clear picture on what will happen inside a Neural Network(NN). But before going through NN, we need to have some knowledge on some of the basic concepts in Calculus(Maths) & architecture of a Neural Network. Note : I recommend you to read the following blog(link mentioned below) and then start reading this blog. Previous blog link : https://arunsdatasphere.blogspot.com/2026/01/deep-learning-and-neural-networks.html At-least try to understand the basic layers of NN, weights, biases, activation function, loss function etc. Lets start with Derivatives. Derivatives : Derivatives are originally a core concept of calculus (maths) . They answer one question which is “How fast is something changing?” Why derivatives appear in Machine Learning ? Machine Learning uses Math as its foundation. In ML, derivatives help answer : If I sligh...

DataSphere

Search This Blog