Feature Engineering & Interpretability for XGBoost with Board Game Ratings
Explore advanced modeling with board game ratings data: feature engineering, XGBoost tuning, and explainability methods for data-driven insights and predictive analysis.
Tuning Random Forest Hyperparameters with Tidymodels
Learn to optimize random forest models using tidymodels, exploring hyperparameter tuning techniques with San Francisco tree data for improved predictive performance.
Medical Polyp Image Segmentation with U-Net and TensorFlow Keras
Step-by-step guide to implement U-Net for medical polyp image segmentation using TensorFlow/Keras, covering data preprocessing, model architecture, training, and evaluation.
TensorFlow Binary and Multi-class Classification Tutorial
Learn TensorFlow for binary and multi-class classification, covering data handling, model building, optimization, and evaluation techniques for effective machine learning implementations.
TensorFlow Tutorial 2022
Comprehensive TensorFlow tutorial covering tensors, data handling, neural networks, and model building. Learn to create, compile, and evaluate machine learning models using practical examples.
Preprocessing - Warping Data
Learn about Correlation Optimized Warping (COW) for aligning misaligned data, including its principles, parameters, and applications in bilinear models and data preprocessing.
Deep Learning for Search and Recommender Systems in Practice - Part 2
Explore advanced techniques in deep learning for search and recommender systems, focusing on practical implementation and real-world applications at LinkedIn.
Hands on Machine Learning - Unsupervised Learning
Explore unsupervised learning techniques including clustering, dimensionality reduction, and Gaussian mixture models. Hands-on implementation using Python libraries for data analysis and visualization.
Unified Understanding of E(3)-Equivariant Interatomic Potentials - Theory and Applications
Explore E(3)-equivariant interatomic potentials, covering theory, applications, and advancements in machine learning approaches for quantum mechanics and molecular dynamics simulations.
Machine Learning and Neural Networks: Data Preprocessing Techniques - Day 3
Explore data preprocessing techniques for machine learning: handling missing data, normalization, standardization, and binarization. Enhance your ML and neural network skills.
Fine-Tuning Alpaca- Train Alpaca LoRa for Sentiment Analysis on a Custom Dataset
Learn to fine-tune Llama 7B with Alpaca LoRa on a custom bitcoin sentiment dataset. Covers data preprocessing, model training, and performance analysis for sentiment detection in cryptocurrency tweets.
How Can Neural Networks Identify Fake Face Images?
Explore neural networks' ability to detect fake face images using Grad-CAM algorithm. Learn about datasets, model training, and visualization techniques for understanding AI decision-making in image classification.
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.
Fixing LLM Hallucinations with Retrieval Augmentation in LangChain
Learn to enhance LLM accuracy using retrieval augmentation in LangChain. Explore data preprocessing, embedding creation, vector database setup, and generative question-answering with citations for improved AI responses.
Machine Learning Tutorial for Beginners
Comprehensive TensorFlow tutorial covering data handling, neural networks, and model building. Learn to clean, analyze, and process data for machine learning applications.
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.
Early Stopping and Encoding a Feature Vector for Deep Neural Networks
Learn to implement early stopping, encode feature vectors, and use validation sets in Keras neural networks for improved deep learning model performance and efficiency.
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.
How to Build Advanced Hand-Gestures Using Radar and TinyML
Explore advanced hand gesture control using radar and Edge AI for headphones, featuring a case study and development process with Imagimob AI software.
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.
Applying Machine Learning Techniques to Detect and Understand Fake News
Explore machine learning techniques for detecting and understanding fake news, covering digital media, web scraping, preprocessing, and classification methods.