Train a PyTorch Neural Network Step-by-Step - PyTorch Deep Learning
Learn to build and train a PyTorch neural network for image classification, achieving 85% accuracy on Fashion MNIST. Covers data preparation, network architecture, loss functions, optimization, and model visualization.
Quantum Algorithms for Classification
Explore quantum algorithms for data classification, focusing on their potential to revolutionize machine learning and tackle complex computational challenges.
Interpretable Machine Learning - Data Brew Season 2 Episode 7
Explore interpretable machine learning with Ameet Talwalkar, discussing its relation to data privacy, fairness, and practical applications in this insightful interview on cutting-edge ML research.
Deep Learning Cross Validation for TensorFlow and Keras
Learn to implement cross-validation techniques for deep learning models using TensorFlow and Keras, enhancing model evaluation and performance in regression and classification tasks.
Introduction to TensorFlow and Keras with Anaconda Python
Learn to build neural networks using TensorFlow and Keras for classification and regression tasks. Covers key concepts, data handling, model creation, training, and evaluation.
Detecting Cancer with RBF Networks Trained by Genetic Algorithms
Explore machine learning techniques for cancer detection using RBF networks and genetic algorithms, with practical insights on implementation and advanced concepts.
Neural Network Architectures for Images
Explore neural network architectures for image processing, including classification, segmentation, and enhancement. Learn about CNNs, ResNets, autoencoders, and their applications in various imaging tasks.
Supervised Machine Learning Review
Comprehensive review of supervised machine learning concepts, focusing on statistical frameworks, loss functions, and the intriguing double-descent phenomenon in neural networks.
Stefan Roth: Robust Scene Analysis
Explore robust scene analysis techniques, focusing on optical flow estimation, motion segmentation, and uncertainty in computer vision tasks. Learn about challenges, objectives, and innovative approaches.
Build Industrial-Grade TinyML Applications with Edge Impulse - Tutorial
Learn to build industrial-grade tinyML applications using Edge Impulse. Covers dataset collection, model design, training, optimization, and integration into embedded systems running on MCUs.
Automated Software Vulnerability Detection with Deep Learning for Natural Language Processing
Explore deep learning techniques for automated software vulnerability detection using natural language processing, covering data sets, pretraining, architecture, and results.
Machine Learning in Fastly's Compute@Edge
Explore WebAssembly modules for efficient ML model execution in Fastly's Compute@Edge, covering wasi-nn extensions, host API revisions, and KServe protocol integration for enhanced performance.
Introduction to Machine Learning - Lecture 2
Explore key machine learning concepts and their applications in healthcare, bridging the gap between computational modeling and clinical problem-solving.
Few-Shot Classification Using Tensor Completion
Explore tensor-based modeling for enhanced classification performance, especially with limited training data, outperforming traditional methods.
Expression Analysis, Clustering, and Classification in Machine Learning - Lecture 2
Comprehensive exploration of machine learning concepts, from basic principles to advanced techniques, with applications in gene expression analysis and text clustering.
Machine Learning for Quantum Matter - Lecture 1
Explore machine learning applications in quantum matter, covering classical algorithms, high-dimensional data, supervised learning, neural networks, and practical examples in physics.
Get Started with AI - A Beginner's Guide to Automated Classification, Predictions and Computer Vision
Apply modern data science techniques for predictions, categorization, and relationship discovery using numeric, textual, and image data. Learn practical AI applications for beginners.
Neuromorphic Language Understanding - Deep Neural Networks for Low-Power Language Processing
Explore recurrent neural networks for language processing and their adaptation to ultra-low power neuromorphic hardware, bridging artificial intelligence with biologically-inspired models of cognition.
Probabilistic Methods for Classification - 2009
Explore probabilistic methods for classification, covering supervised learning, estimation, and advanced models like Naive Bayes, Maximum Entropy, and Conditional Random Fields.
Values and Patterns in Data Learning - 2009
Explore discrete value analysis in data learning through distribution modeling, probability estimation, data compression, and classification. Discover insights from renowned mathematicians and information theorists.
Deterministic Annealing for Clustering, Classification and Speech Recognition
Explore deterministic annealing for improved clustering and classification, avoiding local optima and optimizing various structures in machine learning applications.