Machine Learning for Prediction of Terrestrial Climate and Weather
Explore cutting-edge applications of machine learning in predicting terrestrial climate and weather patterns, presented by a leading expert from the University of Maryland.
LaMini-LM - Mini Models Maxi Data
Explore LaMini-LM: mini language models trained on diverse, large-scale instruction data. Learn about dataset creation, model training, and practical applications through paper analysis and code demonstrations.
How EleutherAI Trains and Releases LLMs - Interview with Stella Biderman
Explore EleutherAI's approach to LLM development, covering model selection, training techniques, GPU choices, and interpretability challenges in this insightful interview with Stella Biderman.
DALL-E Mini - Generate Images From a Text Prompt
Learn about DALL ·E mini, a smaller version of OpenAI's DALL·E that generates images from text prompts. Explore its architecture, training process, and capabilities in this comprehensive overview.
What Are CycleGANs? - A Novel Deep Learning Tool in Pathology
Explore CycleGANs for unpaired image conversion, including applications in pathology and microscopy. Learn model architecture, training, and inference through code demos and real-world examples.
Captcha Recognition Using PyTorch - Convolutional-RNN + CTC Loss
Learn to implement a Convolutional-RNN model for captcha recognition using PyTorch. Covers data preparation, model architecture, CTC loss, and training/inference processes.
How to Train a Deep Learning Model Using Docker
Learn to train deep learning models using Docker, NVIDIA runtimes, and PyTorch for melanoma classification on a single GPU, with practical code implementation and troubleshooting tips.
Cancer Detection in Brain Using Deep Learning
Learn to detect brain tumors using deep learning, CNN, and TensorFlow. Build, train, and test a model for classifying brain images with hands-on coding and practical implementation.
Train StyleGAN with Your Own Images
Learn to train StyleGAN with your own images using Colab, covering image preparation, training setup, and resuming training for both StyleGAN2 and StyleGAN3 models.
Speech-to-Intent on MCU: TinyML for Efficient Device Control - Lecture 6
Learn efficient speech-to-intent techniques for MCUs, focusing on direct parsing of user utterances into actionable output. Explore model training, deployment, and testing on Wio Terminal.
TinyML AutoML Tutorial: Building Production-ready Models with Imagimob AI
Explore Imagimob AI platform for creating production-ready ANN models for time-series data on resource-constrained devices. Learn data visualization, preprocessing, model training, and evaluation techniques.
TinyML Talks Germany - Neural Network Framework Using Emerging Technologies for Screening Diabetic
Innovative framework using neural networks and emerging technologies for efficient screening of Diabetic Retinopathy, achieving high accuracy and significant improvements in energy consumption and latency.
Deploy AI with AWS ML and IoT Services on Jetson Nano
Learn to deploy AI models on Jetson Nano using AWS ML and IoT services. Explore SageMaker, Greengrass, and real-time video processing for edge computing applications.
TensorFlow Models Accelerated for NVIDIA Jetson
Accelerate TensorFlow models on NVIDIA Jetson: optimize training, integrate TensorFlow RT, enhance image classification, and format models for improved performance.
Binarized Neural Networks on Microcontrollers
Explore Binarized Neural Networks for efficient deep learning on microcontrollers, enabling complex tasks with minimal memory and high performance using 1-bit encoding for weights and activations.
Practical Application of TinyML in Battery Powered Anomaly Sensors for Predictive Maintenance of Industrial Assets
Explore tinyML for industrial anomaly detection, enabling predictive maintenance through low-power sensors, wireless tech, and IoT cloud integration. Includes live demo and practical insights.
How to Train and Deploy TinyML Models for Three Common Sensor Types
Learn to train and deploy tinyML models for sensor, audio, and vision data using Edge Impulse. Explore workflows, best practices, and optimization techniques for embedded machine learning applications.
Getting Started with Caffe - Deep Learning Framework Introduction - Class 3
Learn to define, train, and deploy Deep Neural Networks using Caffe framework. Covers data management, network definition, training monitoring, and deployment for classification or feature extraction.
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.
Efficient Large-Scale Language Model Training on GPU Clusters
Explores advanced techniques for efficient large-scale language model training on GPU clusters, discussing parallelism methods, novel scheduling, and performance optimization to achieve unprecedented model sizes and training speeds.
PyTorch 2.0 - Unlocking the Power of Deep Learning with the Torch Compile API
Explore PyTorch 2.0's key features, including backwards compatibility and 43% faster model training. Learn about the torch compile API and its impact on streamlining deep learning development.