Introduction to Deep Learning: Deep Learning Success Stories - Lecture 3
Explore deep learning success stories and their impact across various fields, gaining insights from Prof. A.N. Rajagopalan of IIT Madras.
Fine-Tuning ViT Classifier with Custom Data - Inference for Images and Videos
Fine-tune a ViT model with custom data and perform inference on images and videos. Learn practical techniques for image and video classification using advanced machine learning.
LLMOps: Inference of Fine-Tuned ViT Classifier on CPU with C#
Perform CPU inference on a fine-tuned Vision Transformer using C# and ONNX format for efficient image classification in machine learning projects.
Exploring Shallow Architectures for Image Classification
Explore shallow architectures for image classification, focusing on Scattering Networks and their potential compared to deep CNNs. Examine geometric arguments in deriving competitive representations.
Tackling Covariate Shift with Node-Based Bayesian Neural Networks
Explore Bayesian neural networks for improved generalization under covariate shift, focusing on node-based BNNs as scalable alternatives and their performance in out-of-distribution image classification.
5 Unique Python AI Project Ideas and How to Build Them
Explore 5 Python AI projects from beginner to advanced, with code samples and implementation guidance. Learn sentiment analysis, image classification, voice assistants, recommendation systems, and AI agents.
Neural Structured Learning in TensorFlow
Explore TensorFlow's Neural Structured Learning framework for training robust neural networks with structured signals, applicable to vision, language understanding, and general prediction tasks.
Training Vision Transformers for Real-Time Image Classification
Master Vision Transformer implementation for real-time emotion classification in video, including hands-on training techniques and comparison with CLIP for zero-shot classification tasks.
Understanding I-JEPA: A Non-Generative Approach to Self-Supervised Learning from Images
Dive into Meta AI's I-JEPA framework for self-supervised image learning, exploring its architecture, methodology, and advantages over traditional approaches in computer vision and neural networks.
Introduction to Computer Vision with CNNs
Explore computer vision using CNNs and transfer learning. Learn practical steps for CNN training, including activation functions, and discover applications in image quality assessment and feature extraction.
New in Snorkel Flow 2024.R1: Enhanced Security, Image Categorization, and More
Explore Snorkel Flow's latest release: enhanced security, image categorization, LLM integrations, and document intelligence workflows for advanced AI-driven insights and custom solutions.
Large Language Models Can Help Make Better Image Classifiers
Explore how follow-up differential descriptions enhance zero-shot image classification by tailoring class descriptions, improving performance and addressing ambiguities in datasets.
Fine-Tuning Vision Transformer Classifier for EyePacs Dataset Quality Model - Part 1
Fine-tune a Vision Transformer for image quality assessment using the EyeQ dataset, a subset of EyePacs, to enhance diabetic retinopathy detection capabilities.
Fine-Tuning Vision Transformer for Diabetic Retinopathy Detection - Part 2
Fine-tune a Vision Transformer for diabetic retinopathy detection using EyeQ dataset. Learn to adapt pretrained models for specialized medical image classification tasks.
Integración de MLNet Framework y TensorFlow en C# para Clasificación de Imágenes
Integra MLNet de Microsoft con TensorFlow para clasificación de imágenes en C#. Aprende a utilizar el modelo ImageNet en aplicaciones prácticas.
Microsoft ML.NET Framework Integration with TensorFlow for Computer Vision Classification
Explore MLNet and TensorFlow integration in C# for CV classification using Imagenet model. Hands-on demo with practical code examples.
Fine-Tuning ViT Classifier with Custom Data and Docker Inference - Part 4
Fine-tune a ViT classifier with custom data and deploy using Docker and FastAPI for efficient image classification in production environments.
Leveraging the Power of C++ for Efficient Machine Learning on Embedded Devices
Explore practical C++ and TensorFlow Lite integration for efficient machine learning on embedded devices. Includes image classification and hand gesture recognition demonstrations on Raspberry Pi.
Applying Second-Order Pruning Algorithms for SOTA Model Compression
Explore second-order pruning algorithms for model compression, achieving higher sparsity while maintaining accuracy. Learn to apply these techniques to your ML projects for improved efficiency.
Turing Deployment at Sea - Identifying Plankton in Real Time
Discover how a rapidly developed algorithm for plankton species detection was deployed on a ship, revolutionizing marine research and environmental monitoring.
DIY Computer Vision Image Classification Web App with OpenAI Vision API - Python and Bottle
Build a web app for image classification using OpenAI Vision API, Python, and Bottle. Learn system architecture and code implementation for practical AI integration.