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Medicine and the Arts: Humanising Healthcare
Exploring Play: The Importance of Play in Everyday Life
Songwriting: Writing the Lyrics
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Comprehensive introduction to PyTorch, covering tensor basics, autograd, linear regression, neural networks, and CNNs. Ideal for beginners in deep learning.
Accelerate machine learning with JAX: Learn NumPy-like operations on CPU, GPU, and TPU, automatic differentiation, and high-performance techniques for ML research in this comprehensive introduction.
Explore Python audio processing: work with mp3/wave files, analyze sampling rates, plot waveforms, record audio, and use PyAudio/PyDub libraries for comprehensive audio manipulation.
Learn to build a Generative Adversarial Network from scratch using PyTorch and PyTorch Lightning, covering theory, implementation, and testing in this comprehensive tutorial.
Learn to explore, clean, and prepare data for machine learning using Pandas. Covers handling missing values, outliers, categorical data, and feature preparation for model training.
Learn to set up a Python development environment for AI, including installing Python, managing packages, and configuring VS Code and Jupyter notebooks.
Learn to build a real-time medical transcription analyzer using AssemblyAI's API and Claude 3.5 Sonnet, automating the categorization of key medical terms from live audio.
Learn to create a webapp that summarizes YouTube product reviews using AssemblyAI's LLM framework. Extract key points, generate pros and cons lists, and save time when researching purchases.
Explore insights on balancing risk, innovation, and AI opportunities in legal tech with Ironclad's CEO. Gain valuable perspectives on building successful AI-driven products and strategies.
Learn to build a Flask app for real-time phone call transcription using AssemblyAI, Twilio, and ngrok. Enhance accessibility and gain insights for customer support and call center analysis.
Dive into Replicate's journey in building an open-source AI community. Gain insights on founding challenges, early use cases, impact of open-source LLMs, successful products, and lessons learned in the rapidly evolving AI landscape.
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