Contrastive Learning Courses and Certifications

Adversarial Self-Supervised Contrastive Learning

A presentation of adversarial self-supervised contrastive learning, focusing on robust image representations, adversarial training, evaluation, and experiments.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Contrastive Learning with Adversarial Examples - Spring 2021

A paper-focused overview of contrastive learning with adversarial examples, including unsupervised embeddings, experimental baselines, ablations, and architecture analysis.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

Contrastive Language-Image Pretraining (CLIP) Fluency

Learn how CLIP, a contrastive language-image pretraining model, connects text and images in machine learning.

• Udacity
• 1 hour
• Self-Paced
• Paid Course

Robust Pre-Training by Adversarial Contrastive Learning - CAP6412 Spring 2021

Explore robust model pre-training through adversarial contrastive learning, dual-stream methods, and experiments on semi-supervised training and robustness.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

Black Hat

FACADE - High-Precision Insider Threat Detection Using Contrastive Learning

Discover Google's FACADE AI system for detecting insider threats with 99.99% accuracy using contrastive learning and self-supervised ML on billions of daily events.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Contrastive Learning and History in Face Understanding - Part 1

Explore the fundamentals of contrastive learning techniques and historical developments in facial recognition technology in this introductory session.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

Building Multimodal Search and RAG

44 ratings at Coursera

Build multimodal search and RAG systems: train models with contrastive learning, run any-to-any retrieval, apply visual instruction tuning, and create multi-vector recommenders.

• Coursera
• 1 hour 2 minutes
• Self-Paced
• Free Online Course

Image Generation with AI Training Course

Generate images from text prompts with Stable Diffusion, apply denoising during training and inference, and build shared embedding spaces with autoencoders and contrastive learning.

• Coursera
• 2 weeks, 2 hours a week
• Self-Paced
• Paid Course

Deep Multi-Task and Meta Learning - Autumn 2022

Master multi-task and meta-learning algorithms to build AI systems that efficiently learn new tasks from limited data and transfer knowledge across domains.

• YouTube
• 21 hours 30 minutes
• Self-Paced
• Free Video

CAP6412 Advanced Computer Vision - Spring 2024

Master advanced computer vision through transformers, visual-language models like CLIP and BLIP, multimodal LLMs, and cutting-edge techniques for object detection and image captioning.

• YouTube
• 12 hours 15 minutes
• Self-Paced
• Free Video

Advanced Computer Vision - Spring 2021

Dive into adversarial machine learning, explainable AI, and robust neural network architectures through cutting-edge research papers and advanced computer vision techniques.

• YouTube
• 12 hours 40 minutes
• Self-Paced
• Free Video

Donato Capitella

Multi-Modal LLMs for Image, Sound and Video - Episode 6.3

Explore the architecture and implementation of multi-modal LLMs, focusing on Vision Transformers, contrastive learning techniques, and hands-on experience with Google's PaliGemma.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Neural Breakdown with AVB

Multimodal AI: From First Principles to Neural Networks That See, Hear and Write

Explore the evolution and capabilities of multimodal AI systems, from fundamental principles to advanced models that combine vision, text, and audio for sophisticated machine learning tasks.

• YouTube
• 20 minutes
• Self-Paced
• Free Video

Alfredo Canziani

NYU Deep Learning - Fall 2022

Master neural network inference, energy-based classification, and latent variable models through PyTorch implementations and backpropagation techniques.

• YouTube
• 8 hours 30 minutes
• Self-Paced
• Free Video

Alfredo Canziani

Energy Based Models and Self-Supervised Learning

Explore energy-based models, latent-variable inference, self-supervised learning, and contrastive methods.

• YouTube
• 1 hour 37 minutes
• Self-Paced
• Free Video