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.
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.
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.
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.
Explaining AI to Managers
Demystifying AI for non-technical leaders: Learn to communicate the real value and potential of artificial intelligence in business contexts without delving into complex mathematics.
Astro Pi - Python on the International Space Station
Explore Python programming on the International Space Station with Astro Pi, a collaboration between Raspberry Pi Foundation and ESA, offering exciting opportunities for students to conduct space experiments.
Telehealth Platform - Python & Django Powered
Explore an AI-powered telehealth platform built with Python & Django, featuring digital assistants, voice tech, and ML analytics to enhance doctor-patient interactions and streamline healthcare services.
Deploy Your Machine Learning Bots Like a Boss with CI/CD
Learn to deploy machine learning chatbots using CI/CD, covering load balancing, testing, and model retraining. Gain practical insights for taking prototypes to production and iterating faster.
Docker and Python - Making Them Play Nicely and Securely for Data Science and ML
Learn Docker best practices for data-intensive Python applications, covering image optimization, security, automation, and efficient deployment workflows in Data Science and Machine Learning contexts.
Building Smarter Solutions with No Expertise in Machine Learning
Explore practical ML solutions using existing models and AutoML techniques, with interactive demonstrations and insights for quick implementation without deep expertise.
Decision Science with Probabilistic Programming
Explore generative models and probabilistic programming for decision-making under uncertainty, integrating with robust optimization techniques to enhance business strategies.
Machine Learning for Everyone
Discover the essentials of machine learning, neural networks, and reinforcement learning in this engaging talk. Gain practical insights and witness a mind-blowing demo to kickstart your ML journey.
Painting with GANs - Challenges and Technicalities of Neural Style Transfer
Explore Neural Style Transfer using GANs, including challenges, technicalities, and applications in gaming, fashion, and mobile apps. Learn about loss functions and see code examples.
Why Transformers Work
Explore the inner workings of transformers in machine learning, their advantages over RNNs, and applications in NLP and dialogue handling, with live demos and clear explanations.
The Phantom of Radon
Explore open-source Python library for image reconstruction in Axial Computed Tomography using Radon transforms. Learn about mathematical phantoms, sinograms, and reconstruction algorithms.
Building the Perfect Personalised Menu Using Python
Explore how Gousto uses Python for menu planning optimization and personalized recommendations, leveraging genetic algorithms and graph databases to enhance customer experience and operational efficiency.
30 Golden Rules of Deep Learning Performance
Discover 30 essential guidelines for optimizing deep learning models, enhancing performance, and achieving better results in AI and machine learning projects.
Corona-Net
Explore Corona-Net: an open-source project for COVID-19 diagnosis and symptom segmentation using deep learning models like EfficientNet and U-Net on CT scans.
How Scientific Computing is Advancing the World of Football
Explore how Python's scientific computing and data science applications are revolutionizing football, from tactical analysis to player insights, with potential for wider accessibility and impact.
The Pattern - Machine Learning Natural Language Processing Meets VR/AR
Explore open-source NLP tools and techniques through three ML pipelines using Redis modules. Learn to build knowledge graphs, deploy BERT Q&A models, and implement text summarization with Google's T5.
Production ML Monitoring - Outliers, Drift, Explainers & Statistical Performance
Practical deep dive into advanced ML monitoring techniques, covering outliers, drift, explainers, and statistical performance for robust production deployment and scalability.