From Paper to Product - How We Implemented BERT
Explore the journey of implementing BERT in a natural language generation product, from initial research to production deployment. Learn about challenges, successes, and valuable lessons for NLP practitioners.
Dive into Deep Learning with Scala
Explore deep learning fundamentals and implementation in Scala, covering neural networks, computer vision, and practical applications with hands-on examples and library comparisons.
Differentiable Functional Programming
Explore differentiable functional programming, covering supervised learning, gradient descent, and automatic differentiation techniques for efficient deep learning implementations in Scala.
Differentiable Functional Programming
Explore differentiable functional programming, covering supervised learning, gradient descent, and deep learning concepts. Learn about tensor operations, algorithmic differentiation, and Scala's potential in next-gen deep learning systems.
Machine Learning for Front-End Developers
Explore machine learning in the browser for front-end developers. Learn applications, tools, and resources with practical demos to start building your own JavaScript ML experiments.
Automatically Categorizing News Articles on NRK.no
Explore machine learning techniques for automatic news article categorization, including word embeddings, clustering, and supervised learning, with insights on real-world implementation in production environments.
SW Testing - Can ML Save Us?
Explore the potential of machine learning in software testing, addressing challenges in GUI testing and discussing promising applications like supervised learning and reinforcement learning.
Introduction to ML.NET
Explore ML.NET for infusing custom AI into .NET apps. Learn about scenarios, architecture, and hands-on examples like GitHub issue classification and content-based filtering.
Machine Learning - The Bare Math Behind Libraries
Explore the mathematical foundations of machine learning, from supervised to unsupervised learning techniques, to gain a deeper understanding of neural networks and their underlying processes.
Machine Learning Fundamentals - Lecture 1.3
Explore machine learning concepts and applications in AI, focusing on knowledge representation and reasoning techniques.
Practical Paths to Data-Centric AI
Explore data-centric AI with Google experts, covering data quality, feature selection, and large language model deployment. Learn about synthetic data, weak supervision, and data governance.
Thinking of Topic Modeling as Search in Real-time Applications
Explore how treating topic modeling as a search problem can enhance real-time applications and overcome common challenges in document classification and clustering.
Introduction to the Perceptron Algorithm - Lecture 8
Dive into the fundamental concepts of the Perceptron algorithm, exploring its core mechanisms and applications in neural network foundations.
Linear Models and Their Geometry in Machine Learning - Lecture 5
Explore the fundamental concepts and geometric principles behind linear classifiers, a cornerstone of machine learning algorithms and classification techniques.
The Learning Machine: Demonstrating Machine Learning on Apple II
Explore fundamental machine learning concepts using an Apple II, demonstrating supervised learning, metaheuristics, and reinforcement techniques through practical examples.
Tackling Advanced Classification Using Snorkel Flow
Explore advanced classification techniques using Snorkel Flow, addressing challenges in label schema, high cardinality, and multi-label problems while leveraging foundation models for production-ready solutions.
Introduction to Deep Learning - Traditional Approaches vs Deep Learning - Lecture 2
Explore deep learning fundamentals, comparing traditional and modern approaches in machine learning and NLP.
K-Nearest Neighbors (KNN) Algorithm - Introduction and Applications
Explore k-Nearest Neighbors algorithm: concepts, applications, assumptions, implementation, and optimization techniques for effective machine learning.
Logistic Regression to Deep Learning Neural Network
Explore the evolution from logistic regression to deep neural networks, understanding key concepts and applications in machine learning and AI.
Learning about Machine Learning
Explore machine learning fundamentals with expert Roger D. Amos in this concise 25-minute lecture from QCHEM LAB at KNU.
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective - UAI 2023 Oral Session 4
Explore theoretical analysis of learning from low-rank tensor data in supervised and unsupervised settings, quantifying performance gains of tensor methods over standard vector approaches.