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1. Google-Landmark-Recognition Google-Landmark-Recognition Public

   Google Landmark recognition is a famous image recognition problem for the past few years. The task is given an image the model has to recognize the landmark(if any) in the image and then accordingl…

   Jupyter Notebook

2. TF-IDF-and-Clustering-based-models-for-retrieving-documents-given-a-query- TF-IDF-and-Clustering-based-models-for-retrieving-documents-given-a-query- Public

   Given a corpora of documents, we design two types of models. One based on Term Frequency and Inverse Document Frequency. Another model based on clustering algorithms like K-means and Agglomerative…

   Jupyter Notebook

3. Denoising-Autoencoder-and-Fine-Tuning- Denoising-Autoencoder-and-Fine-Tuning- Public

   In this project I intend to design a De-noising autoencoder model (M1) for the MNIST Dataset. Next I design a good classification model (M2) for the FMNIST Dataset. Using M1 as pre-trained base mod…

   Jupyter Notebook

4. Detecting-and-Defending-Denial-of-service-Attack-in-Software-defined-Network Detecting-and-Defending-Denial-of-service-Attack-in-Software-defined-Network Public

   Assuming an existing software defined network. This project attempts to detect a denial of service attack in the network.

   Python

5. Single-Cycle-32-bit-MIPS-Processor Single-Cycle-32-bit-MIPS-Processor Public

   In this project we implement a single cycle 32 bit MIPS based processor in Verilog language. The processor supports various types of instructions that include Immediate (I) type, Register (R) type …

   Verilog

6. Shell-Interpreter-in-C-to-simulate-a-set-of-LINUX-commands Shell-Interpreter-in-C-to-simulate-a-set-of-LINUX-commands Public

   This project involves designing a shell interpreter, which takes Linux commands and arguments as inputs and parses them then executes the commands. Few commands that are implemented include History…