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The chapter delves into the essential tools and technologies necessary for AI development, highlighting key Python libraries, interactive development environments, and outlining the AI development lifecycle. It emphasizes the significance of structured workflows in building AI systems to ensure effective outcomes. By leveraging powerful libraries such as TensorFlow, PyTorch, and Scikit-learn, along with platforms like Jupyter Notebooks and Google Colab, practitioners can accelerate AI innovation and deployment.
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Sections
Class Notes
Memorization
What we have learnt
- Python is the most widely u...
- Key libraries such as Tenso...
- The AI development lifecycl...
Revision Tests
What we have learnt
- Python is the most widely used programming language in AI development due to its simplicity and extensive library ecosystem.
- Key libraries such as TensorFlow, PyTorch, and Scikit-learn are crucial for different facets of AI tasks.
- The AI development lifecycle involves stages like problem definition, data preparation, model development, evaluation, deployment, and ongoing maintenance.
Key Concepts
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Term: TensorFlow
Definition: An open-source library developed by Google for machine learning that offers tools for building and training neural networks.
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Term: PyTorch
Definition: A dynamic computation graph library by Facebook's AI Research lab, preferred for its ease of use and suitable for research and rapid prototyping.
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Term: Scikitlearn
Definition: A user-friendly library for traditional machine learning algorithms, ideal for beginners and smaller-scale machine learning projects.
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Term: Jupyter Notebooks
Definition: An open-source web application that enables interactive coding, visualizations, and narrative text, primarily used for data analysis.
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Term: Google Colab
Definition: A cloud-based Jupyter Notebook environment that provides free access to computing resources for training AI models.
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Term: AI Development Lifecycle
Definition: A structured workflow that includes stages such as problem definition, data preparation, model development, evaluation, deployment, and monitoring.