kumarnikhil936/malicious_url_detector_autoencoder

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kumarnikhil936/malicious_url_detector_autoencoder

kumarnikhil936

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The idea is to employ deep learning method (autoencoder) to build a URLs classification model, which will be trained on normal (non malicious) URLs dataset. The resulting classifier will be able to distinguish between a malicious and a non malicious URL.

supervised_pca

Jupyter Notebook

face_emotion_recognition_cnn

Jupyter Notebook

logic_simulator

The goal of this project is to develop and implement a simple logic and delay simulator. Theprogram should be able to read in circuits in the benchmark format (.bench files) and translate them to an internal netlist representation.

anomaly_detection_guest_talk

Presentation and Notebook for the Tech-Talk at STTP MRIIRS

Jupyter Notebook

edge_computing_tflite

Using TensorFlow Lite, an easy solution for running machine learning models on mobile and embedded devices. It enables on‑device machine learning inference with low latency and a small binary size on Android and other embedded platforms.

imdb_movie_review_classifier

Performed sentiment analysis and text classification on the IMDB dataset.

tf_idf_tutorial

For building any natural language models, the key challenge is how to convert the text data into numerical data. As the machine learning or deep learning models don’t understand the text data.Here we work with the TF-IDF method which is short for “term frequency–inverse document frequency”. This TF-IDF method is the popular word embedding technique used in various natural language process tasks.

Jupyter Notebook

health_companion_medical_chatbot

Inspiration is partly from the challenges of the Freiburg Hackathon 2020, as well as from our own values and desires.

alexnet_lenet_vgg16_keras

Implemented various neural network models like Alexnet, Lenet, and VGG16 for the task of face recognition. The dataset used is a slightly different variant of the LFW dataset. Also given here is the support to save your models in h5 file format and later use it to create a tflite model to be run on embedded device.

comparing_autoencoder_flavors

Comparing the different variants of Autoencoders and evaluating their performance on the MNIST dataset.

Jupyter Notebook

facial_recognition_inception_network

This implementation is based on the Facenet paper published by Google, which proposes the idea of using inception module (basically inception network) for the task of facial recognition. This method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches where the training is done on the complete picture rather than the face area only. To train, triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. However, here we will be using the pretrained weights which is uploaded here as well for one's easy access. The benefit of this approach is much greater representational efficiency, since face recognition performance is using only 128-bytes per face.