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Practical AI
Understanding the landscape of AI techniques
Jul 30, 2018
Jared Lander, data science guru, discusses the landscape of AI techniques, the strengths of R language for ML/AI, and the usage of R in industries like finance and healthcare. The speakers also talk about democratizing AI and making ML accessible to developers with tools like Plumber and Algorithmia.
44:46
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Quick takeaways
- R is a versatile language for AI projects, offering easy data preparation, analysis, and powerful data visualization.
- R provides packages for implementing deep learning models, allowing users to build and train complex neural networks for various AI applications.
Deep dives
R and AI: A Natural Fit
The R language has a long-standing history in the data science and machine learning community, making it a natural fit for AI projects. R was specifically designed for handling and manipulating data, allowing for easy data preparation and analysis. The R community is vibrant and supportive, with meetups and conferences offering opportunities to connect with like-minded individuals. R has strong capabilities in machine learning, with packages like 'carrot' providing a unified interface for various machine learning algorithms. The integration of R with TensorFlow and Keras has brought deep learning capabilities to the R ecosystem, enabling the development, training, and deployment of neural networks. Additionally, R offers powerful data visualization and reporting functionalities, making it a versatile language for AI projects.
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