Grow Your Business with Player Analytics for Mobile Games
Discover how Player Analytics provides game developers with crucial insights into player behavior, spending patterns, and churn prediction to optimize gameplay and boost business growth.
Creating Rolling Sum Grouped by ISO Weeks Using Python - No Pandas Required
Learn to implement weekly rolling sums using Python's standard library, exploring itertools' groupby, enumerate, and isocalendar. Enhance your data manipulation skills without relying on external libraries like pandas.
TaskWarrior Command Line Productivity Scoreboard - Build with Python
Build a CLI productivity scoreboard for TaskWarrior using Python's standard libraries. Learn data manipulation techniques without external dependencies like pandas or numpy.
Rapid PySpark Custom Processing on Time Series Big Data in Databricks
Efficient PySpark processing for time series big data, enabling rapid custom calculations on rolling windows with constant time complexity, demonstrated using Sleep Number Smartbed sensor data.
Alerting and Anomaly Detection - Best Friends Forever?
Explore the relationship between alerting and anomaly detection in monitoring systems, discussing their strengths, limitations, and optimal integration for effective system management.
Forecasting Weather with Neural Prophet and Python
Learn to forecast weather using Neural Prophet and Python. Preprocess data, train models, and generate accurate temperature predictions with this hands-on tutorial.
LSTM Time Series Prediction Tutorial Using PyTorch in Python - Coronavirus Daily Cases Forecasting
Learn to forecast Coronavirus cases using LSTM and PyTorch. Covers data preprocessing, model building, training, evaluation, and future predictions with real-world time series data.
GPT-4 Code Interpreter: 18+ Use Cases for Data Analytics and Visualization
Explore GPT-4's Code Interpreter: image editing, data analytics, 3D modeling, and more. Discover 18+ use cases for professionals, students, and enthusiasts to enhance their projects with AI-powered tools.
Learning from Dependent Data - Lecture 2
Explores techniques for analyzing and learning from dependent data, focusing on probabilistic and optimization methods in data science.
No Data, No Problem - Giving Nuclear Inspectors Better Tools Without Revealing State Secrets
Explore privacy-preserving computation in nuclear safeguards, focusing on garbled circuits for anomaly detection without compromising sensitive information between inspectors and facilities.
Time Series Forecasting with XGBoost - Advanced Methods
Explore advanced time series forecasting with XGBoost, covering outlier removal, cross-validation, lag features, and future predictions. Enhance your skills in handling complex time-dependent data for improved forecasting accuracy.
Time Series Forecasting with XGBoost - Use Python and Machine Learning to Predict Energy Consumption
Learn to predict energy consumption using XGBoost and Python in this hands-on tutorial. Covers data preparation, feature engineering, model building, and forecasting techniques for time series analysis.
Recurrent Neural Networks Explained Easily
Explore Recurrent Neural Networks: architecture, time series, and Back Propagation Through Time. Learn about simple RNN units and their applications in sequence processing tasks.
Practical AI Solutions Within Healthcare and Biotechnology - Michael Segala
Explore AI applications in healthcare, including image analysis, NLP, and time-series data. Learn about challenges, workflows, and real-world use cases in diagnostics and patient care.
ODSC West 2015 - Hacking Machine Data
Explore techniques for analyzing machine data, including Hidden Markov Models and anomaly detection. Learn to handle time-series datasets with high volatility and noise, applying real-world methods to extract valuable insights.
Methods for Scalable Probabilistic Inference - IPAM at UCLA
Explore scalable probabilistic inference methods for astrophysics, focusing on Gaussian Processes for time series analysis and open-source tools to accelerate Bayesian inference with large datasets.
Scalable AutoML for Time Series Forecasting Using Ray
Explore scalable AutoML for time series forecasting using Ray, covering feature generation, model selection, and hyper-parameter tuning. Learn from real-world applications and experiences in various industries.
Deep Learning II - Joan Bruna NYU
Explore advanced deep learning concepts including symmetry, transformations, and geometric stability in neural networks with NYU professor Joan Bruna.
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.
Resource Efficient Machine Learning in a Few KBs of RAM - tinyML Summit 2020
Explore resource-efficient machine learning techniques for IoT devices with limited RAM, focusing on Microsoft's EdgeML library and algorithms like ProtoNN and FastRNN for edge computing.
Cloud-Native Metric Monitoring with Prometheus
Explore cloud-native metric monitoring with Prometheus, covering its key features, concepts, and integration with Grafana Labs' tools for comprehensive observability and efficient system management.