Deep Learning — Prelims Strategy

Prelims Strategy

For Prelims, the strategy for Deep Learning should focus on conceptual clarity and factual recall. Begin by firmly grasping the hierarchy: AI > Machine Learning > Deep Learning. Understand the core components of a neural network (neurons, layers, weights, biases, activation functions).

Crucially, differentiate between key architectures: CNNs for images, RNNs for sequences, and Transformers for advanced NLP with self-attention. Memorize the purpose of backpropagation. Be familiar with prominent model examples like AlexNet, ResNet, BERT, and the GPT family, associating them with their primary applications and architectural innovations.

Pay close attention to Indian initiatives: NITI Aayog's National AI Strategy, National AI Portal, and National AI Mission – know their objectives and key features. Current affairs related to AI, especially generative AI (ChatGPT, Bard) and AI regulation debates, are high-yield areas.

Practice MCQs that test definitions, applications, and the differences between related concepts. Focus on keywords like 'convolutional,' 'recurrent,' 'attention mechanism,' 'backpropagation,' and 'algorithmic bias.

' A strong foundation in these basics will help navigate tricky options and trap questions.