Stuff Sector
Prepared by
Santhosh (Admin)
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UNIT I DEEP NETWORKS BASICS Vectors, Matrices and tensors; Probability Distribution Gradientbased Optimization, ML (Overfitting and underfitting)*** Bias and variance Stochastic gradient descent Challenges motivating deep learning**Deep feedforward networks,Optimization.***
UNIT II CONVOLUTIONAL NEURAL NETWORKS
Convolution Operation, Sparse Interactions *** Equivariance, Pooling, Strided Transposed and dilated convolutionsNonlinearity Functions, Loss Functions*** Regularization , Gradient Computation.***
UNIT III RECURRENT NEURAL NETWORKS
RNN Design Patterns, Acceptor, Encoder,Transducer Bidirectional RNN, Sequence to Sequence RNN*** Deep Recurrent Networks, Recursive Neural Networks***Long Term Dependencies,Gated Architecture: LSTM.
UNIT IV MODEL EVALUATION
Baseline Models , Automatic Hyperparameter Grid search , Random search**Debugging strategies.*** May be part c UNIT V AUTOENCODERS AND GENERATIVE MODELS
Undercomplete ,Regularized autoencoders ** rare Stochastic encoders and decoders ***Learning Abt and Variational autoencoders **Generative adversarial networks.Don't share as screenshot
**Most important topic ,Can get good marks but read all topic thoroughly
**Most important topic ,Can get good marks but read all topic thoroughly
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*These questions are expected for the exams This may or may not be asked for exams All the best.... from admin Santhosh
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Syllabus
UNIT I DEEP NETWORKS BASICS
Linear Algebra: Scalars -- Vectors -- Matrices and tensors; Probability Distributions -- Gradientbased 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
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
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
Performance metrics -- Baseline Models -- Hyperparameters: Manual Hyperparameter -- Automatic
Hyperparameter -- Grid search -- Random search -- Debugging strategies.
UNIT V AUTOENCODERS AND GENERATIVE MODELS
Autoencoders: Undercomplete autoencoders -- Regularized autoencoders -- Stochastic encoders
and decoders -- Learning with autoencoders; Deep Generative Models: Variational autoencoders –
Generative adversarial networks.