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Explore all talks and presentations from MLCon. Dive deep into the latest insights, research, and trends from the world's leading experts.
Explore strategies for building data pipelines from scratch, leveraging open data, web-sourced information, and collected data to create innovative data products that address customer needs in data-scarce environments.
Explore the frontier of machine learning in web browsers, covering state-of-the-art libraries, implementation challenges, and performance optimization techniques for browser-based ML applications.
Explore the potential commoditization of AI, its challenges, and implications for businesses. Learn about cloud AI services and strategies for AI startups in a competitive landscape.
Explore the transformative potential of IoT data and AI in industry, focusing on Reinforcement Learning for industrial control tasks, from energy management to product quality optimization.
Explore real-time AI trading systems, algorithmic architecture, and overcoming challenges in building scalable, robust AI for multi-instrument trading and cross-sector correlation analysis.
Explore natural language understanding with LUIS for building conversational UIs and bots. Learn fundamental concepts, live coding demos, and practical applications in this comprehensive talk.
Dive into LSTM implementation using TensorFlow's Python API for natural language understanding. Learn the math and code behind this powerful RNN architecture in a hands-on session.
Explore integrating TensorFlow models into Java enterprise environments, covering deployment, version control, and integration with popular server frameworks like Spring and Apache CXF.
Explore deep learning fundamentals, including matrix algebra and calculus, and get practical tips for using frameworks like Keras, PyTorch, and TensorFlow in this informative session.
Practical insights on developing a Deep Learning Android app, from data collection to production deployment using TensorFlow, with tips on choosing the right approach and best practices.
Exploring user-centric AI design: How to create socially integrated, accepted, and beneficial machine learning systems for future businesses and daily life.
Explore security risks in AI/ML adoption, focusing on secure coding practices in Python. Learn techniques to improve code hygiene and protect against vulnerabilities in ML-based solutions.
Explore AI-driven career recommendations using multivariate matching algorithms. Learn how to identify suitable careers, quantify specialization, and gain insights into career progression paths beyond your current field.
Explore Kotlin's potential in machine learning projects, from data cleaning to model deployment. Learn about Kotlin tools for data science, including Jupyter notebooks, visualizations, and dataframes.
Explore TinyML: train, convert, and run machine learning models on microcontrollers. Learn quantization, pruning techniques, and step-by-step integration for embedded AI applications.
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