Social And Emotional Learning Courses

tinyML

Efficient On-Device Deep Learning: Challenges and Opportunities

Explore efficient on-device deep learning, focusing on affordable AI models for edge devices and maximizing inference performance using heterogeneous computing resources.

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

tinyML

Machine Learning at the Extreme Edge of Always-on Intelligent Sensor Networks

Explore ML optimization techniques for resource-constrained IoT sensor nodes, focusing on system, algorithm, architecture, and circuit levels to achieve significant area-power improvements.

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

tinyML

Edge Machine Learning for Mobile Health Technologies

Edge machine learning techniques for resource-constrained mobile health and wearable technologies, addressing challenges and opportunities in IoT systems for real-time health monitoring and early detection of abnormalities.

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

Paul Hand

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.

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

Eran Feit

Transfer Learning Using Keras

Learn to classify car brands using transfer learning with Keras and ResNet50. Hands-on tutorial covers dataset preparation, model configuration, training, and prediction visualization.

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

Eran Feit

Convolutional Neural Networks - Deep Learning Basics with Python, TensorFlow and Keras

Learn to build and train a convolutional neural network for chess piece image classification using Python, TensorFlow, and Keras. Covers dataset preparation, model creation, and prediction testing.

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

Eran Feit

Deep Learning in Weather Images - Using TensorFlow and CNN

Learn to build a CNN model using TensorFlow and Keras to predict weather scenes from images. Covers dataset preparation, model creation, training, and testing.

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

Eran Feit

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.

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

Eran Feit

How to Classify Flowers Using Deep Learning

Learn to build a convolutional neural network for flower image classification using TensorFlow and Keras, from dataset preparation to model testing and prediction.

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

Jeff Heaton

Setting Up CUDA, CUDNN, Keras, and TensorFlow on Windows 11 for GPU Deep Learning

Comprehensive guide for setting up GPU-accelerated deep learning environment on Windows 11, covering CUDA, CUDNN, Keras, and TensorFlow installation with step-by-step instructions.

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

Jeff Heaton

How to Choose an NVIDIA GPU for Deep Learning - Quadro, Ampere, GeForce Compared

Comprehensive guide to selecting NVIDIA GPUs for deep learning, comparing Quadro, Ampere, and GeForce models. Covers cooling, memory, NVLink, and multi-GPU setups for optimal performance.

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

Jeff Heaton

Introduction to Q-Learning for Game Play - Lecture 12.2

Explore Q-Learning for game AI: build policy tables, suggest optimal actions, and apply Python techniques for environment-based decision-making in this introduction.

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

Jeff Heaton

Using FFMPEG to Encode-Decode Video for Offline Video-Based Machine Learning

Learn to use FFMPEG for video encoding/decoding in machine learning, enabling frame-by-frame processing and video generation from individual images.

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

Jeff Heaton

Multi-Variate Feature Engineering Presentation for Machine Learning

Learn advanced feature engineering techniques for machine learning, including feature importance, target coding, neural networks, and embedding layers to improve model performance and extrapolation.

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

Jeff Heaton

How Much Data Is Enough to Build a Machine Learning Model

Explore techniques to measure data coverage for machine learning models, ensuring sufficient multivariate representation and understanding extrapolation in high dimensions.

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