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50 interview questions on deep learning and machine learning (AI & ML)
- Natural Intelligence encompasses innate cognitive abilities in organisms to solve problems, adapt, and learn, contrasting Human Intelligence in creating tools and innovation.
- Key milestones in AI history include the Turing Test, AI coinage in 1956, AI winters in the 1960s-70s, and AI breakthroughs with Deep Blue, Watson, and Transformers.
- AI comprises Machine Learning (ML) for pattern learning, Data Science for insight extraction, and Deep Learning for complex pattern modeling.
- Structured vs. unstructured data differentiation, supervised vs. unsupervised learning models, and challenges of data growth in AI are notable discussions.
- Parametric vs. non-parametric models, machine learning types (supervised, unsupervised, reinforcement), and data distribution concepts provide foundational AI knowledge.
- Decision trees in machine learning, ensemble methods like bagging and boosting, and clustering and dimensionality reduction in unsupervised learning are essential topics.
- Deep Learning, activation functions, optimization algorithms like gradient descent, and hyperparameter tuning form the core of neural network development.
- RNNs, LSTMs, and GRUs for sequential data, CNNs for image processing, and foundation models leading the AI paradigm shifts are key advancements.
- Dropout and batch normalization for regularization, max pooling in CNNs, and innovations in optimization algorithms are significant in deep learning.
- Generative AI, LLMs like GPT-3, fine-tuning models, and ethical considerations in AI development are crucial advancements in AI and ML fields.
- State-of-the-art AI developments encompass reinforcement learning, hybrid DL/ML methods, and AI ethics solutions focusing on privacy, transparency, and bias.
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