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Course Detail

Course Name Deep Learning
Course Code 19CCE334
Program B. Tech. in Computer and Communication Engineering
Year Taught 2019


Unit 1

Artificial Neural Networks- The Neuron-Expressing Linear Perceptrons as Neurons-Feed-Forward Neural Networks- Linear Neurons and Their Limitations –Sigmoid – Tanh – and ReLU Neurons -Softmax Output Layers – Training Feed-Forward Neural Networks-Gradient Descent-Delta Rule and Learning Rates- Gradient Descent with Sigmoidal Neurons- The Backpropagation Algorithm-Stochastic and Minibatch Gradient Descent – Test Sets – Validation Sets – and Overfitting- Preventing Overfitting in Deep Neural Networks – Implementing Neural Networks in TensorFlow.

Unit 2

Local Minima in the Error Surfaces of Deep Networks- Model Identifiability- Spurious Local Minima in Deep Networks- Flat Regions in the Error Surface – Momentum-Based Optimization – Learning Rate Adaptation.

Unit 3

Convolutional Neural Networks(CNN) – Architecture -Accelerating Training with Batch Normalization- Building a Convolutional Network using TensorFlow- Visualizing Learning in Convolutional Networks – Embedding and Representation Learning -Autoencoder Architecture-Implementing an Autoencoder in TensorFlow –DenoisingSparsity in Autoencoders Models for Sequence Analysis – Recurrent Neural Networks- Vanishing GradientsLong Short-Term Memory (LSTM) Units- TensorFlow Primitives for RNN Models-Augmenting Recurrent Networks with Attention.


  • Nikhil Buduma, “Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithm”, O’Reilly, 2017.
  • Ian Goodfellow, YoshuaBengio and Aaron Courville, “Deep Learning”, MIT Press, 2016.


  • AurélienGéron, “Hands-On Machine Learning with Scikit- Learn and TensorFlow”, O’Reilly, 2017.
  • Nikhil Ketkar, “Deep Learning with Python: A Hands-on Introduction”, Apress, 2017.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

Objectives and Outcomes


  • To introduce the idea of artificial neural networks and their architecture
  • To introduce techniques used for training artificial neural networks
  • To enable design of an artificial neural network for classification
  • To enable design and deployment of deep learning models for machine learning problems

Course Outcomes

  • CO1: Able to understand the mathematics behind functioning of artificial neural networks
  • CO2: Able to analyze the given dataset for designing a neural network based solution
  • CO3: Able to carry out design and implementation of deep learning models for signal/image processing applications
  • CO4: Able to design and deploy simple TensorFlow-based deep learning solutions to classification problems

CO – PO Mapping

CO1 3 3
CO2 3 2 3
CO3 2 3 2 3
CO4 2 2 2 2 2

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