Get productive with TensorFlow.js - Google's Machine Learning library for JavaScript. From pre-made off the shelf models to writing or training your own, learn how to create next gen web apps.
What you'll learn
Common terms and what they mean
How Machine Learning works (without formal mathematical definitions)
Overview of the TensorFlow.js library
Advantages of using ML in JavaScript
Ways to consume or create Machine Learning models
How to use pre-made “off the shelf” models
What Tensors are in Machine Learning
How to use Tensors with ML models
How to write a simple custom model
Perceptrons (artificial neuron) and how they work
Linear regression to predict numbers using single neuron
Multi layered perceptrons for handling more complex data
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
This is CS50W, CS50's Web Programming with Python and JavaScript. Register for free at https://cs50.edx.org/web. Slides and source code at https://cs50.harvard.edu/web. This course picks up where Harvard University’s CS50 leaves off, diving more deeply into the design and implementation of web apps…
This course explores the world of Transformers and Large Language Models (LLMs). You will learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications. Throu…
Dive into the intriguing world of AI Transformers with this curated YouTube playlist. Featuring expert insights, educational content from leading organizations, and engaging discussions, this playlist keeps you informed about the latest breakthroughs in Transformer technology.
Stanford CS25: Transformers United Since their introduction in 2017, transformers have revolutionized Natural Language Processing (NLP). Now, transformers are finding applications all over Deep Learning, be it computer vision (CV), reinforcement learning (RL), Generative Adversarial Networks (GANs)…