Fundamentals of Deep Learning Lab (AIML355)
Lab record templates aligned with the university experiment list. Replace placeholders with your dataset source, code, outputs, plots, observations, and viva answers. (Minimum: 8 experiments to be performed.)
Course Objectives
- Implement deep learning models in Python and train them with real-world datasets.
- Implement CNN, RNN and Deep Learning NLP models in Python.
Course Outcomes
- CO1: Design and implement CNN for object classification from images/video.
- CO2: Implement Autoencoder, RNN, LSTM (variants) and Deep NLP.
Find an experiment
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Tip: Keep the same write-up order in every experiment (Aim → Dataset → Procedure → Code → Output → Discussion → Viva).
Exp 1 — TensorFlow & Keras basics
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Exp 2 — ANN for regression & classification
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Exp 3 — CNN for MRI dataset
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Exp 4 — Autoencoder for dimensionality reduction
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Exp 5 — Autoencoder on image dataset
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Exp 6 — Conv layers performance (MNIST)
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Exp 7 — RNN for stock price prediction
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Exp 8 — LSTM weather prediction
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Exp 9 — Transfer learning (MobileNetV2)
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Exp 10 — Transfer learning (VGG16)
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Exp 11 — NLP analysis of restaurant reviews
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Exp 12 — Spam detection with TF-IDF
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