Dadang Amir Hamzah’s Post

I just finished recording and uploading a video on Youtube titled: "Understanding Chain Rule: The backbone of neural networks" I spent 2 days designing this lecture and coming up with practical examples to demonstrate why we use the Chain Rule. It's a simple rule of calculus, which forms the backbone of gradient descent in neural networks. I believe that in mastering neural networks: 20% of the concepts lead to 80% of the understanding. Chain rule definitely comes in that 20%. Here is the video recording: https://lnkd.in/eFCKumiy Hope you have fun learning this concept! Do ask in the comments section here or on the Youtube video if you have any doubts or questions.

Here is a good book about the basics of machine learning and neural networks: https://lnkd.in/d3wwP2za

I'm excited to share my latest Medium article: "Backpropagation Visuals: Neural Networks Don’t Have to Be a Black Box". In this post, I dive into how visualizing the backpropagation process can demonstrate neural networks and provide clearer insights into their inner workings.

Activation Functions: The Gatekeepers of Neural Networks. Check out my latest article on Medium: https://lnkd.in/dJEb-J4w When Activation Functions Invited to Party , The Fun learning : #Deeplearning #machinelearning #activationfunctions

It seems a lot of people want a primer on AI. So here's a really good series of videos by a strong maths fellow explaining things in a very simple, very clear way. https://lnkd.in/ezHh4dct Three Blue One Brown, in case you're not familiar, does a lot of really good deep dives on all kinds of math problems, Tranformers, neural networks, deep learning, etc being only one small part of the channel's content. It will also give you a clue as to why I say machines are thinking now. A lot of people have simplified their understanding of LLMs as "next token predictors" which masks what's going on there. Markov chains are next token predictors. Transformers are, yes, at the end giving a list of tokens and the likelihood of each, but to get to that point requires a lot of sophisticated thought. To grok that, watch this video series and get a sense of the complexity that's hidden underneath transformers. This does not go into diffusion models which are thinking machines in a different sense. If I find a good video series on those, I'll post it.

🚀 Lesson 2: Neural Network (Layers) Section 2 🧠. Uncover how layers serve as the fundamental building blocks of neural networks. #ArtificialIntelligence #NeuralNetworks #DeepLearning

What Are 3 Examples Of Neural Network? #NeuralNetworks #MachineLearning #ArtificialIntelligence #AI #DeepLearning #TechInnovation #DataScience #AIApplications #TechTrends #FutureTech #Computing #TechExplained #AIResearch #SmartTech #Innovation https://lnkd.in/ebXqe4eD

Learnt along this tutorial by Andrej Karpathy, the tutorial revolved around the basics of deep learning and how neural networks are built and worked with from ground up. tutorial - https://lnkd.in/gtZ2FH-S More details on my github repo - https://lnkd.in/gScRdzZp

🤔 Day 1: Being foreign to how ML models work fundamentally, I begin my applied GenAI studies with theory 😅 Today, I am sharing a playlist that has been highly recommended for understanding the concept with visual aids - from explaining neural networks, layers, parameters, training, and more. Happy learning #GeneraiveAI #0to100xengineer

A short video on image style transfer: “Image Style Transfer Using Convolutional Neural Network” Video link: https://lnkd.in/eVVa2Ttt Channel link: https://lnkd.in/ehtNhSPW