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Set up a Python environment for AI development with conda and pip, VS Code, local notebooks, and Google Colab.
A hands-on introduction to PyTorch covering tensors, autograd, training loops, neural networks, and convolutional neural networks.
A practical introduction to JAX for accelerating Python and NumPy computations with just-in-time compilation, automatic differentiation, vectorization, and parallelization.
Learn to load, inspect, plot, record, and manipulate audio files using Python.
Build a generative adversarial network from scratch in PyTorch, implementing generator–discriminator training and testing on MNIST.
Use pandas to explore, clean, and prepare a tabular flight-delay dataset for machine-learning model training.
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.
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.
Master the creation of real-time AI voice agents by integrating DeepSeek R1, AssemblyAI for speech-to-text, and ElevenLabs for text-to-speech, enabling natural conversational interactions through Python programming.
Discover how Model Context Protocol (MCP) standardizes AI agent interactions with external resources, from basic LLM limitations to advanced integrations, with practical code examples and implementation strategies.
Master prompt engineering techniques for Universal-3 Pro to improve audio transcription accuracy with live examples, real-world tips, and domain-specific vocabulary handling.
Discover production insights from founders who've deployed millions of voice agent calls, covering tech stack redundancy, voicemail detection, and call quality measurement.
Discover neural networks, transformers, and NLP fundamentals through comprehensive tutorials covering backpropagation, regularization, RNNs, and modern LLM techniques.
Master Python fundamentals for AI development through hands-on projects covering data manipulation, ML model training, HuggingFace integration, and API usage.
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