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.
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.
The Power of Deep Learning in R
Deep learning, a powerful technique for training neural networks with multiple hidden layers, has gained popularity in recent years. R provides several packages, such as 'neuralnet', 'mxnet', and 'keras', that allow for the implementation of deep learning models. These packages enable users to build and train complex neural networks, leverage the nonlinear activation functions, and capture intricate data patterns. The integration of TensorFlow with R through the 'keras' package provides seamless access to the TensorFlow deep learning framework from within the R ecosystem. With deep learning in R, users can tackle a wide range of AI applications, from image and speech recognition to natural language processing and more.
Taking R Models to Production
R has made significant progress in making it easier to deploy machine learning models into production environments. Tools like 'Plumber' and third-party solutions like Algorithmia allow users to convert R scripts into APIs, making it simple for developers to consume the results of R models in their applications. These tools facilitate the operationalization of R models, enabling easy integration with other software systems. By providing APIs and interoperable data formats, the R community is focused on democratizing AI and making it accessible to a wider range of developers and users. This allows for seamless collaboration and maximizes the value of AI models by enabling their integration into various business solutions.
Exciting Advancements in R Beyond AI
While AI and machine learning are exciting areas in the R community, there are also other noteworthy advancements happening. R Markdown, for instance, provides a powerful tool for automating slide decks and generating reports with interactive elements. R packages like 'HTMLWidgets' enable the embedding of JavaScript visualizations into R reports, enhancing the communication and presentation of data insights. The R community is also focused on data manipulation, network analysis, and other data science areas. By exploring these diverse aspects of R, users can unlock new possibilities and find innovative ways to analyze, visualize, and communicate their data.
Jared Lander, the organizer of NYHackR and general data science guru, joined us to talk about the landscape of AI techniques, how deep learning fits into that landscape, and why you might consider using R for ML/AI.
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