Lab Facilities

DEEP LEARNING LABORATORY

OBJECTIVES:

• To understand the tools and techniques to implement deep neural networks.
• To apply different deep learning architectures for solving problems.
• To implement generative models for suitable applications.
• To learn to build and validate different models.

OUTCOMES:

 After the completion of this course, students will be able to:

• Apply deep neural network for simple problems.
• Apply Convolution Neural Network for image processing.
• Apply Recurrent Neural Network and its variants for text analysis.
• Apply generative models for data augmentation.
• Develop real-world solutions using suitable deep neural networks.

Software:

• Python/Java with Machine Learning packages
  

BIG DATA ANALYTICS

OBJECTIVES: 

• To understand big data.
• To learn and use NoSQL big data management.
• To learn mapreduce analytics using Hadoop and related tools.
• To work with map reduce applications.
• To understand the usage of Hadoop related tools for Big Data Analytics.

OUTCOMES:

After the completion of this course, students will be able to:

• Describe big data and use cases from selected business domains.
• Explain NoSQL big data management.
• Install, configure, and run Hadoop and HDFS.
• Perform map-reduce analytics using Hadoop.
• Use Hadoop-related tools such as HBase, Cassandra, Pig, and Hive for big data analytics.

Software:

• Cassandra, Hadoop, Java, Pig, Hive and HBase