Dash - Interactive Data Visualization Web Apps with No Javascript
Learn to create interactive data visualization web apps using Dash, a Python framework that requires no JavaScript. Discover best practices, common pitfalls, and when to transition to traditional web development.
Boosting Research with Machine Learning
Explore machine learning applications in research, from neuron recognition to EEG-based movement prediction. Learn Python techniques for data preparation and analysis using various ML methods.
Distributed Multi-GPU Computing with Dask, CuPy and RAPIDS
Explore distributed multi-GPU computing using Dask, CuPy, and RAPIDS. Learn to leverage GPU acceleration and distributed systems for faster, more efficient data processing and analysis.
How to Train an Image Classifier Using PyTorch
Learn to build an image classifier using PyTorch in this hands-on tutorial. Covers data preparation, model selection, training techniques, and evaluation, with practical tips and code examples for immediate implementation.
Classification Based on Missing Features in Deep Convolutional Neural Networks
Explore a novel approach to enhance neural network accuracy and robustness in adversarial situations, focusing on classification based on missing features in deep convolutional models.
Securely Executing Python Machine Learning Models with Distroless Images at ING
Explore secure execution of Python ML models using distroless images at ING. Learn about security risks, minimal Docker images, and practical implementation in a banking environment.
Supercharge Your Deep Learning Algorithms with Optimized Software
Optimize deep learning algorithms on Intel architecture using Intel® MKL for training and Intel® nGraph and OpenVINO™ for inferencing, enhancing runtime performance.
Modern Data Science with Vaex - A New Approach to DataFrames and Pipelines
Explore efficient data processing for large datasets using Vaex, combining computational graphs and lazy evaluations with memory-mapped storage and out-of-core algorithms.
Dissecting tf.function to Discover AutoGraph Strengths and Subtleties
Explore AutoGraph and tf.function in TensorFlow 2.0, learning key concepts for efficient graph-convertible code and improved performance in deep learning applications.
Build Your Python Extensions with Rust
Explore writing Python extension modules in Rust using pyo3 and milksnake libraries. Learn about performance, safety, and maintainability compared to C and Cython extensions.
TensorFlow 2.0 - TensorFlow Strikes Back
Explore TensorFlow 2.0's new features, including Keras API, eager execution, and improved production pipeline, enhancing usability while maintaining performance and scalability.
Building Industry 4.0 Logistics Applications with MicroPython and ESP32 MCUs
Explore how MicroPython and ESP32 MCUs can create innovative Industry 4.0 logistics solutions, focusing on a forklift hailing application. Learn about prototyping, deployment, and live demonstration.
AI in Contemporary Art
Explore AI's impact on contemporary art, from creative applications like DeepDream to critical examinations of facial recognition and deep fakes. Discover how artists and technologists are pushing boundaries and addressing societal implications.
Deploy Python to the Cloud Faster with Azure Serverless
Learn to deploy and scale Python workloads using Azure Functions, with seamless integration in Visual Studio Code for local development, debugging, and cloud deployment. Explore serverless computing benefits and management tools.
Audio Classification with Machine Learning
Learn to build practical machine learning models for audio classification using spectrograms, CNNs, and techniques like transfer learning and data augmentation with Keras and TensorFlow.
Natural Language Processing with Neural Networks
Dive deep into NLP with neural networks, exploring architectures, feature engineering, and practical tips for optimal results while considering computational costs and interpretability.
When a Biologist Met Python
Explore the intersection of biology and Python, discovering how computational tools enhance biological research and improve scientific workflows.
The State of Machine Learning Operations in 2019
Overview of key challenges and trends in machine learning productization, covering reproducibility, explainability, and orchestration. Includes insights on open-source tools and frameworks for tackling these issues in ML operations.
Building Data-Driven Client Relationship Management in Banking with Python
Transforming banking sales with Python: data-driven client management, machine learning pipelines, and automated processes for improved efficiency and personalized service.
When a Biologist Met Python
Explore the intersection of biology and Python, discovering how this programming language revolutionizes research in computational biology and bioinformatics through various modules and tools.
Opening PyPy's Magic Black Box
Explore PyPy's meta-tracing JIT compiler, understanding its optimization techniques for faster Python code execution. Gain insights into PyPy's inner workings and learn to leverage its performance benefits.