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Master AI-powered music creation through generative techniques, from Markov chains to transformers, targeting both technologists and musicians seeking creative applications.
Master SOLID principles to write cleaner, more maintainable machine learning code with practical examples and real-world applications for ML engineers.
Master PyTorch and torchaudio for deep learning in audio and music processing, including neural networks, mel spectrograms, CNNs, and GPU-accelerated sound classification.
Master audio signal processing fundamentals and extract features from raw audio data using Python to build powerful AI applications with hands-on implementation techniques.
Master melody generation using Long Short-Term Memory networks to create AI-composed music from preprocessing datasets to converting neural network outputs into MIDI files.
Detailed breakdown of Google's MusicLM, exploring its architecture, training, and capabilities in generating music from text prompts. Insights on its impact and limitations in AI-driven music creation.
Create a Spotify-like music recommender system using Python, collaborative filtering, and Alternating Least Squares. Learn about music recommendation types, Spotify's architecture, and implement artist suggestions with the 'implicit' library.
Insights into a pioneering community-driven research project: organization, phases, and lessons learned from collaborative paper publishing in AI and sound generation.
Learn to deploy Machine Learning models in production using BentoML, covering installation, model saving, service creation, bento building, and Docker containerization. Includes hands-on examples and practical deployment steps.
Explore innovative AI music projects from The Sound of AI Hackathon, covering diverse areas like music recommendation, audio generation, DJ-ing, and denoising.
CJ Carr explores neural audio synthesis, sharing experiences with Dadabots and insights on generating music using neural networks. He discusses open-source models and various applications of AI in music creation.
Learn to process audio in Keras using Kapre, exploring its advantages, features, and implementation. Discover how to embed audio transformations within deep learning models for optimized GPU-based calculations and easier deployment.
Explore DeepMind's Gato, a revolutionary AI agent capable of 600+ tasks. Discover its potential for general intelligence, performance analysis, and the ethical implications of advancing towards AGI.
Explore recursion in Python through creative applications like generating music, fractals, and sentences. Learn implementation techniques and understand recursive concepts with practical examples.
Implement audio data augmentation techniques in Python, including noise addition, time stretching, pitch scaling, polarity inversion, and random gain, with practical code examples.
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