Implementing Capped Collections in MongoDB with Pymongo

Implementing Capped Collections in MongoDB with Pymongo

Create high-throughput MongoDB collections with Pymongo using capped collections. Maintain insertion order, overwrite old data once full. Ideal for logging systems with constant write operations. Tailable cursor for real-time data streams. Limitations, but performance benefits make them suitable for specific use cases. Example command included.

The post Implementing Capped Collections in MongoDB with Pymongo appeared first on Python Lore.

Working with Embeddings in Keras

Working with Embeddings in Keras

Maximize efficiency and enhance categorical data representation with embeddings in Keras. Learn how these powerful features capture semantic relationships and reduce dimensionality, making them ideal for natural language processing applications. Explore the use of pre-trained embeddings for optimal results.

The post Working with Embeddings in Keras appeared first on Python Lore.

Creating Custom Statistical Distributions in scipy.stats.rv_continuous

Creating Custom Statistical Distributions in scipy.stats.rv_continuous

Create and work with custom statistical distributions using scipy.stats.rv_continuous in the SciPy library. Define custom probability density functions (PDFs) and other statistical functions for accurate representation of complex real-world phenomena. Import rv_continuous, define a subclass, and explore methods for custom PDFs and random number generators.

The post Creating Custom Statistical Distributions in scipy.stats.rv_continuous appeared first on Python Lore.