Page 102 - B.E CSE Curriculum and Syllabus R2017 - REC

Department of CSE, REC

REFERENCES:
1. Stefan Buttcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval Implementing and
Evaluating Search Engines, The MIT Press, Cambridge, Massachusetts London, England, 2010
2. http://www.search-engines-book.com/slides/.

CS17E84 DEEP LEARNING L T P C
3 0 0 3
OBJECTIVES:
 To acquire knowledge on the basics of neural networks.
 To implement neural networks using computational tools for variety of problems.
 To explore various deep learning algorithms.
 To implement Neural Networks using Tensorflow.
 To know the various applications of Deep Learning.

UNIT I CONVOLUTIONAL NEURAL NETWORKS 9
Neurons in Human Vision-The Shortcomings of Feature Selection-Vanilla Deep Neural Networks Don’t
Scale-Filters and Feature Maps-Full Description of the Convolutional Layer-Max Pooling-Full Architectural
Description of Convolution Networks-Closing the Loop on MNIST with Convolutional Networks-Image
Preprocessing Pipelines Enable More Robust Models-Accelerating Training with Batch Normalization-
Building a Convolutional Network for CIFAR10-Visualizing Learning in Convolutional Networks -
Leveraging Convolutional Filters to Replicate Artistic Styles-Learning Convolutional Filters for Other
Problem Domains.

UNIT II MEMORY AUGMENTED NEURAL NETWORKS 9
Neural Turing Machines-Attention-Based Memory Access-NTM Memory Addressing Mechanisms-
Differentiable Neural Computers-Interference-Free Writing in DNCs-DNC Memory Reuse-Temporal Linking
of DNC Writes-Understanding the DNC Read Head-The DNC Controller NetworkVisualizing the DNC in
Action-Implementing the DNC in TensorFlow-Teaching a DNC to Read and Comprehend.

UNIT III DEEP REINFORCEMENT LEARNING 9
Deep Reinforcement Learning Masters Atari Games - Reinforcement Learning -Markov Decision Processes
(MDP)-Explore Versus Exploit-Policy versus Value Learning-Pole-Cart with Policy Gradients-Q-Learning
and Deep Q-Networks-Improving and Moving Beyond DQN.

UNIT IV IMPLEMENTING NEURAL NETWORKS IN TENSORFLOW 9
Introduction to TensorFlow – Comparitive analysis of Tenforflow - Installing TensorFlow-Creating and
Manipulating TensorFlow Variables-TensorFlow Operations-Placeholder Tensors-Sessions in TensorFlow-
Navigating Variable Scopes and Sharing Variables-Managing Models over the CPU and GPU-Specifying the
Logistic Regression Model in TensorFlow-Logging and Training the Logistic Regression Model-Leveraging
TensorBoard to Visualize Computation Graphs and Learning-Building a Multilayer Model for MNIST in
TensorFlow.

UNIT V APPLICATIONS OF DEEP LEARNING 9
Deep learning for computer vision – Data Augmentation - Neural Language Models - High-Dimensional
Outputs – Health care applications.
TOTAL: 45 PERIODS
OUTCOMES:
At the end of the course, student will be able to:
 Develop algorithms simulating human brain.
 Implement Neural Networks in Tensor Flow for solving problems.
 Explore the essentials of Deep Learning and Deep Network architectures.
 Apply reinforcement
 Define, train and use a Deep Neural Network for solving real world problems that require artificial
Intelligence based solutions.

Curriculum and Syllabus | B.E. Computer Science and Engineering | R2017 Page 102