<ul><li>Artificial intelligence's role is increasing, with companies implementing AI for various tasks.</li><li>While Python is prominent in ML, Elixir is used in real-time processing and distributed systems.</li><li>Neural networks, inspired by the brain, consist of interconnected nodes processing data.</li><li>Machine learning models learn from data to perform tasks without specific instructions.</li><li>Key concepts include models (linear, neural networks), tensors, learning types (supervised, unsupervised), and hyperparameters.</li><li>Evaluation metrics like accuracy, precision, recall, and regression metrics assess model performance.</li><li>Elixir tools like Nx for computation, Axon for neural networks, and Explorer for data processing are discussed.</li><li>Bumblebee offers pre-trained models, Nx.Serving deploys models, and LiveBook aids in interactive development.</li><li>Scholar provides classical ML algorithms in Elixir, while Phoenix seamlessly integrates ML models in web apps.</li><li>Elixir's advantages include distributed computing, real-time processing, and reliability for ML applications.</li></ul>

Elixir for Machine Learning. Part 1 - Theory

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