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What technologies has John Mosquera been using for his personal AI projects in the last six months?
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In the last six months, John Mosquera has been working on personal AI projects using a wide range of technologies and libraries. These include TensorFlow and PyTorch for deep learning, Scikit-Learn for traditional machine learning tasks, and Keras for building neural networks. He has also used Hugging Face Transformers for natural language processing (NLP) tasks and LangChain for building AI-driven applications.For data manipulation and analysis, John has relied on Pandas and NumPy. He has used Weights & Biases (W&B) and MLflow for experiment tracking and model management. Additionally, he has leveraged CUDA and TPUs for accelerated training, and cloud-based AI services like Azure AI Studio, Google AI Studio, and AWS Bedrock for scalable AI solutions.John’s use of these technologies demonstrates his ability to work with both open-source and cloud-based tools, ensuring flexibility and efficiency in his AI projects. John Mosquera has been using TensorFlow, PyTorch, Scikit-Learn, Keras, Hugging Face Transformers, LangChain, Pandas, NumPy, Weights & Biases, MLflow, CUDA, TPUs, Azure AI Studio, Google AI Studio, and AWS Bedrock for his personal AI projects. His use of these technologies ensures flexibility and efficiency in building AI-driven solutions.