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Neural Net Architectures Explained
Deep dive into CNNs, RNNs, Transformers, and their core building blocks. Understand the 'why' behind each.
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ML Model Selection Workshop
Guide to choosing the right ML model for your specific problem and data. Practical considerations.
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Code Walkthrough: Simple NN
Hands-on coding session: building and training a basic neural network from scratch with Python.
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Ethical AI & Bias in Models
Discussing fairness, accountability, and transparency in deep learning systems. Identifying and mitigating bias.
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Future of AI: Trends & Research
Exploring emerging research areas, novel applications, and the trajectory of intelligent systems.
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Troubleshooting DL Models
Common challenges in deep learning (overfitting, vanishing gradients) and practical solutions.