AI School’s Post

Started watching this series on ML by Andrew NG - https://lnkd.in/gc3m7Vue it’s finest introduction in the word of AI. It’s Mathematics heavy that’s why it is making more interesting for deep dive. 

Working in AI/ML has shown me just how essential matrices are to every aspect of our models—from neural networks to linear regression and PCA. They are the building blocks that make data transformation, optimization, and problem-solving possible. Mastering these mathematical concepts is key to unlocking deeper insights and building better AI systems. #MachineLearning #AI #DataScience #LinearAlgebra #TechInnovation #MatrixMath #AILeadership #PCA #MathematicsInAI #RavenR

Learning the math behind machine learning earns you the agility to adapt to industry trends and ensure your model is consistently providing maximal value to your company. Let me know in the comments if you’d like to learn more about how any of the following play into ML: - calculus - linear algebra - graph theory - activation functions - topology of data #ml #ai #math

Becoming Experts in Matrices: The Foundation of Machine Learning Matrix analysis is the foundation of machine learning, whether you're using it for neural network training, linear regression, or dimensionality reduction methods like PCA. From fundamental operations like multiplication and addition to complex concepts like: Using Cramer's Rule to solve linear equations, Principal Component Analysis (PCA) to reduce dimensionality, identity matrices for model regularisation, It's all made possible by matrices! They are the unseen sources driving the optimisations and data transformations that drive contemporary AI systems. Do you want to utilise machine learning to its maximum potential? Start with linear algebra; a grasp of matrices can revolutionise the development of more accurate, scalable, and efficient models.  Check this article out ! #MachineLearning #AI #DataScience #LinearAlgebra #TechInnovation #MatrixMath #AILeadership #PCA #MathematicsInAI #RavenR https://lnkd.in/de8AwU3U   

Machine learning allows computers to learn from historical data, detect patterns in the data using algorithms, and make decisions or predictions accordingly. Types of Machine Learning Supervised Learning Unsupervised Learning Reinforcement learning To learn each type with examples just check the article, which is simplified for understanding.

This was a very interesting introduction course in DataCamp for learning to use Pytorch to train deep-learning models!

I wrote a paper : " Reinforcement Learning: A Historical and Mathematical Overview (1950-2024)" You can download it here: https://lnkd.in/dFEdMkhB AIFI - Artificial Intelligence Finance Institute

Reinforcemnet Learning (RL) will continue to shape the future of Artificial Intelligence (AI) As it is the only branch of AI with a framework to learn new skills/make smarter decisions. Here is a nice write-up on the theory of RL. But how about getting started with hands-on Python coding to implement RL? Start Here: 1️⃣ 𝗥𝗟 𝟭𝟬𝟭: Build Q-Learning from scratch in Python (Cristian Leo): https://shorturl.at/iwtzL 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗥𝗟 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻: (credit to Nicolò Cosimo Albanese) https://shorturl.at/Lj7hp 3️⃣ 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗥𝗟 𝗮𝗻𝗱 𝗣𝘆𝘁𝗵𝗼𝗻: https://shorturl.at/M7BT5 4️⃣ 𝗥𝗟 𝗶𝗻 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: RLHF-like approach, DPO (credit to Maxime Labonne) https://shorturl.at/4N2Kc #artificialintelligence #datascience

I'm thrilled to announce that I've earned my certificate in "Introduction to Machine Learning: Supervised Learning"! Machine learning has always fascinated me, and this course has provided me with a solid foundation in supervised learning, a critical aspect of this transformative field. From understanding core concepts to exploring practical applications, this learning journey has been both enlightening and empowering. Throughout the course, I delved into the fundamentals of supervised learning, gaining insights into classification, regression, and real-world applications across diverse domains. Armed with this knowledge, I'm excited to apply supervised learning techniques to solve complex problems and drive innovation in my work. As I continue to explore the vast landscape of machine learning, I'm grateful for the opportunity to expand my skill set and stay at the forefront of this rapidly evolving field.  #MachineLearning #SupervisedLearning #DataScience #AI #ContinuousLearning

This course helped me learn the basics of a neural network and how to code it, introducing me to machine learning!