Posted inasyncio Python modules
Creating and Managing Coroutines with asyncio
Master Python coroutines with asyncio. Learn to define `async def` functions, manage the event loop, and avoid common traps in asynchronous programming.
Posted inPython modules PyTorch
Custom Loss Functions and Advanced Regularization Techniques
Regularization techniques like L1/L2 penalties, dropout, batch normalization, weight decay, and early stopping control model complexity to prevent overfitting. Balancing model size, data quality, and regularization strength is essential for improving generalization and achieving reliable machine learning performance.
Posted inhttp Python modules
Creating CGI HTTP Server with http.server.CGIHTTPRequestHandler
A simple to-do list web app with a Python and Flask server backend. The server provides GET and POST API endpoints for managing items with JSON. The front-end uses vanilla JavaScript with fetch and async/await to dynamically update the DOM and perform optimistic updates for a fast UI.
Posted inPython modules TensorFlow
Implementing Convolutional Neural Networks with tf.keras.layers.Conv2D
Keras Conv2D parameters for optimal CNN performance. Analysis of kernel_size, filters, padding, and BatchNormalization. Also covers efficient SeparableConv2D layers and kernel_regularizer to combat overfitting. This approach improves accuracy, speed, and training stability in your network.
Posted inPython modules SciPy
Clustering and Spatial Analysis with scipy.cluster
Hierarchical clustering limits on large datasets due to O(n²) complexity. K-means scales better, especially with subsampling or scikit-learn’s MiniBatchKMeans for faster clustering. Memory optimization via float32 reduces footprint. Distributed computing with Dask enables large-scale spatial data processing.
Posted inPandas Python modules
Exploring pandas.DataFrame.memory_usage for Memory Optimization
Python performance and memory profiling for Pandas data optimization. Use cProfile to find bottlenecks and memory_profiler for line-by-line memory analysis. The most efficient method is specifying dtypes in pd.read_csv during initial data loading to avoid memory spikes.
Posted inPillow (PIL Fork) Python modules
Integrating Pillow with GUI Frameworks for Image Display
A PyQt custom widget for a performant, pannable, zoomable image viewer. Manage view state with pan offset and zoom factor. Use QPainter in paintEvent for efficient, GPU-accelerated rendering of high-resolution QPixmap objects without blocking event handlers like mouseMoveEvent or wheelEvent.
Posted inFlask Python modules
File Upload Handling in Flask
Flask secure file serving for files stored outside the web root. Use the send_from_directory function to create a secure endpoint, add authorization, prevent directory traversal, and stream files. Force downloads using as_attachment=True and specify a custom attachment_filename.
Posted injson Python modules
Customizing JSON Encoding with json.JSONEncoder
Custom JSONEncoder subclassing for flexible serialization of complex Python objects like custom classes and datetime. Override default() to convert objects to JSON-serializable formats, enabling centralized, reusable encoding logic for json.dumps with support for multiple types including Complex and datetime.
Posted inmath Python modules
Using math.ldexp to Calculate x * (2**i)
Floating-point normalization using math.ldexp and math.frexp enables efficient scaling by powers of two, reducing overflow risk and rounding errors. Applications include dataset normalization, constructing subnormal and large floats for testing, and optimized vector scaling in numerical algorithms and simulations.
