Anna University – AD3501 Deep Learning Regulation 2021 Syllabus , Notes Book , Important Questions, Question Paper with Answers Previous Year Question Paper.
UNIT I DEEP NETWORKS BASICS AD3501 Deep Learning Book
Linear Algebra: Scalars — Vectors — Matrices and tensors; Probability Distributions — Gradient-based
Optimization – Machine Learning Basics: Capacity — Overfitting and underfitting –Hyperparameters
and validation sets — Estimators — Bias and variance — Stochastic gradient descent — Challenges
motivating deep learning; Deep Networks: Deep feedforward networks; Regularization —
Optimization.
UNIT II CONVOLUTIONAL NEURAL NETWORKS AD3501 Deep Learning Syllabus
Convolution Operation — Sparse Interactions — Parameter Sharing — Equivariance — Pooling —
Convolution Variants: Strided — Tiled — Transposed and dilated convolutions; CNN Learning:
Nonlinearity Functions — Loss Functions — Regularization — Optimizers –Gradient Computation.
UNIT III RECURRENT NEURAL NETWORKS AD3501 Deep Learning Notes
Unfolding Graphs — RNN Design Patterns: Acceptor — Encoder –Transducer; Gradient Computation
— Sequence Modeling Conditioned on Contexts — Bidirectional RNN — Sequence to Sequence RNN
– Deep Recurrent Networks — Recursive Neural Networks — Long Term Dependencies; Leaky Units:
Skip connections and dropouts; Gated Architecture: LSTM.
UNIT IV MODEL EVALUATION AD3501 Deep Learning Important Questions
Performance metrics — Baseline Models — Hyperparameters: Manual Hyperparameter — Automatic
Hyperparameter — Grid search — Random search — Debugging strategies.
UNIT V AUTOENCODERS AND GENERATIVE MODELS AD3501 Deep Learning Question paper
Autoencoders: Undercomplete autoencoders — Regularized autoencoders — Stochastic encoders
and decoders — Learning with autoencoders; Deep Generative Models: Variational autoencoders –
Generative adversarial networks.
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| Important Questions | Click Here |
| Previous Year Question Paper | Click Here |
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TEXT BOOK AD3501 Deep Learning Book
1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016.
2. Andrew Glassner, “Deep Learning: A Visual Approach”, No Starch Press, 2021.
REFERENCES AD3501 Deep Learning Notes
1. Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun, “A Guide to
Convolutional Neural Networks for Computer Vision”, Synthesis Lectures on Computer Vision,
Morgan & Claypool publishers, 2018.
2. Yoav Goldberg, “Neural Network Methods for Natural Language Processing”, Synthesis Lectures
on Human Language Technologies, Morgan & Claypool publishers, 2017.
3. Francois Chollet, “Deep Learning with Python”, Manning Publications Co, 2018.
4. Charu C. Aggarwal, “Neural Networks and Deep Learning: A Textbook”, Springer International
Publishing, 2018.
5. Josh Patterson, Adam Gibson, “Deep Learning: A Practitioner’s Approach”, O’Reilly Media, 2017