Introduction to QGIS
Learn QGIS 3 for geospatial analysis: create 3D models, analyze terrain, visualize data, and produce maps. Master essential GIS skills for various applications in this comprehensive introduction.
Imagery and Remote Sensing
Learn the basics of observing and understanding our planet with the help of Arc Gis.
How to Perform Large-Scale Image Classification
Learn how Kaggle Grandmasters built large-scale image classification models for landmark recognition, overcoming challenges of 81,000+ classes. Discover winning strategies, architectures, and techniques for efficient modeling and competition success.
Deep Learning Using Matlab
Explore deep learning concepts and implement neural networks, including CNNs, using MATLAB for practical applications in image classification and beyond.
Creating a Machine Learning GUI App with Taipy and TensorFlow
Build a stylish GUI for image classification using Taipy and TensorFlow. Learn to process images, create neural networks, and design user-friendly interfaces for machine learning applications.
Practical Machine Learning with Tensorflow
We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow.
Observational Supervision for Medical Image Classification
Exploring gaze data as a novel supervision source for training deep learning models in medical image classification, focusing on chest X-rays and brain MRI scans for improved efficiency and accuracy.
Stanford Seminar 2022 - DeepMind's Perceiver and Perceiver IO: New Data Family Architecture
Exploring DeepMind's Perceiver architecture for flexible AI processing across diverse data types, offering domain-general solutions comparable to specialized models in various tasks.
Neural Networks
This playlist has everything you need to know about Neural Networks, from the basics, all the way to image classification with Convolutional Neural Networks.
Bag-of-Features for Image Classification - Lecture 17
Learn about Bag-of-Features in computer vision, covering image classification, feature distribution, clustering, and support vector machines for visual recognition tasks.
Unsupervised Meta-Learning for Few-Shot Image Classification
Explore unsupervised meta-learning techniques for few-shot image classification, including task construction, augmentation methods, and performance on benchmark datasets.
Computer Vision: Classification and Object Recognition - Lecture 11
Explore computer vision classification techniques, including object recognition, image classification, and machine learning frameworks. Learn about nearest neighbor and linear classifiers.
Bag of Words Model for Image Classification - Lecture 16
Explore bag-of-features for image classification, including feature extraction, quantization, and spatial pyramid matching. Learn about datasets and applications in scene and action recognition.
Classification in Computer Vision - Lecture 18
Explore classification in computer vision, covering features, linear classification, nearest neighbor, decision boundaries, and maximum margin classifiers. Gain insights into fundamental algorithms and problem-solving approaches.
Introduction to Convolutional Neural Networks - Part I - Lecture 8
Introduction to convolutional neural networks, covering architecture, applications in computer vision and natural language processing, with examples like AlexNet and super-resolution techniques.
Introduction to Neural Networks for Computer Vision - Part I
Introduction to neural networks for computer vision, covering key concepts like pixel representation, feature extraction, and image classification. Explores biological inspiration and computational implementation of neural activation functions.
Computer Vision - Administrative and Course Overview - Lecture 1
Introduction to graduate-level computer vision, covering mathematical foundations, deep learning applications, and advanced topics like object detection and image segmentation.
Image Classification with ml5.js - MobileNet and Custom Models
Dive into image classification using ml5.js 1.0 in this comprehensive 32-minute video tutorial by Coding Train. Learn to implement both the default MobileNet model and train your own classifier using transfer learning and Teachable Machine. Explore the f…
Production Machine Learning Monitoring: Outliers, Drift, Explainers and Statistical Performance
Dive deep into advanced ML model monitoring techniques, including outlier detection, concept drift, explainability, and performance metrics for robust production deployments.
Deploying Efficient Data-Centric AI 100x Faster
Accelerate AI application development and deployment 100x using dedicated platforms, data labeling, pre-built models, and iterative design. Explore region classification and deep-trained object detection approaches.
Deploying Efficient Data-Centric AI 100x Faster
Accelerate AI application development and deployment 100x using dedicated platforms, data labeling, pre-built models, and AI-automation. Explore region classification and deep-trained object detection approaches.