ManishKumarKeshri

8puzzle

There exists a puzzle state which comprises of 8 numbers and an empty slot E randomly arranged. The goal state is to reach the puzzle state that will have the numbers arranged in order with the 0 coming after all other numbers have been arranged. For example, consider the initial puzzle state as (E 1 3 4 2 5 7 8 6), then the final or the goal state will be (1 2 3 4 5 6 7 8 E). We should be using A* algorithm to solve the 8-puzzle problem with the heuristic being the total number of misplaced tiles in the given puzzle state. The empty slot can move only once to generate the new puzzle state.

Apple-Share-Price-Prediction

Built an LSTM model to predict apple share price. Trained it past 6 months daily price data.

Automatic-attendance-taking-using-CCTV-Camera

Implementation of Haar-Cascades algorithm for frontal face and eyes detection and Fisherfaces algorithm for face recognition using OpenCV C++ Library

Boston-Home-Prices-Prediction-and-Evaluation

Iteratively Re-Weighted Least Squares method based Logistic method

CUDA-enabled-Parallel-Implementation-of-Collaborative-Filtering

Achieved speed-up of 402x in similaritymatrix calculation on NVIDIA GTX 1080; used coalescing, thread coarsening and shared memory tiling.

Danube-Web

Decipher-sign-language-using-Deep-Neural-Network

Built a deep neural network with Batch Normalization and tuned Hyperparameters that would facilitate communications from a speech-impaired person to someone who doesn’t understand sign language (Used: Python: scikit-learn, TensorFlow) - Used dataset of images provided by Coursera to train to test the algorithm (Accuracy on -Train: 0.91, -Test: 0.84)

Distributed-SVD

Simulated a geo-distributed recommendation system using a decentralized SVD algorithm

pravega

Pravega - Streaming as a new software defined storage primitive

Trigger-Word-Detection

Constructed a speech dataset and implemented an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection). Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word.

ManishKumarKeshri/Trigger-Word-Detection

Constructed a speech dataset and implemented an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection). Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word.

ManishKumarKeshri/Automatic-attendance-taking-using-CCTV-Camera

Implementation of Haar-Cascades algorithm for frontal face and eyes detection and Fisherfaces algorithm for face recognition using OpenCV C++ Library

ManishKumarKeshri/Boston-Home-Prices-Prediction-and-Evaluation

Iteratively Re-Weighted Least Squares method based Logistic method

ManishKumarKeshri/CUDA-enabled-Parallel-Implementation-of-Collaborative-Filtering

Achieved speed-up of 402x in similaritymatrix calculation on NVIDIA GTX 1080; used coalescing, thread coarsening and shared memory tiling.

ManishKumarKeshri/Decipher-sign-language-using-Deep-Neural-Network

Built a deep neural network with Batch Normalization and tuned Hyperparameters that would facilitate communications from a speech-impaired person to someone who doesn’t understand sign language (Used: Python: scikit-learn, TensorFlow) - Used dataset of images provided by Coursera to train to test the algorithm (Accuracy on -Train: 0.91, -Test: 0.84)

ManishKumarKeshri/Distributed-SVD

Simulated a geo-distributed recommendation system using a decentralized SVD algorithm

ManishKumarKeshri/8puzzle

There exists a puzzle state which comprises of 8 numbers and an empty slot E randomly arranged. The goal state is to reach the puzzle state that will have the numbers arranged in order with the 0 coming after all other numbers have been arranged. For example, consider the initial puzzle state as (E 1 3 4 2 5 7 8 6), then the final or the goal state will be (1 2 3 4 5 6 7 8 E). We should be using A* algorithm to solve the 8-puzzle problem with the heuristic being the total number of misplaced tiles in the given puzzle state. The empty slot can move only once to generate the new puzzle state.

ManishKumarKeshri/Apple-Share-Price-Prediction

Built an LSTM model to predict apple share price. Trained it past 6 months daily price data.

ManishKumarKeshri/Danube-Web

ManishKumarKeshri/pravega

Pravega - Streaming as a new software defined storage primitive

ManishKumarKeshri/Data-Mining-of-Transactions

• Implemented FP Tree algorithm to find frequently purchased itemsets and corresponding association rules in more than 63000 transactions using C++ • Compared results for different values of minimum support and confidence.

ManishKumarKeshri/Deep-Learning-Experiments

Notes and experiments to understand deep learning concepts

ManishKumarKeshri/Deep-Neural-Network-for-Image-Classification

Built a deep network, and applied it to classify images of cat vs non-cat.

ManishKumarKeshri/Dogs_vs_cats_image_recognition

CNN based image recognition model using tflearn

ManishKumarKeshri/FINDING-FRAUD-IN-ENRON-EMAIL-DATA

• Explored dataset with 146 data points (i.e. ”employees of Enron”), each of which has 21 features( i.e. email info, salary, compensation etc.) to find the fraud. • Implemented features selection, outliers removal, classification and validation. • Compared confusion matrices and F1 scores of Decision tree, SVM and AdaBoost classifiers (Used: Python).

ManishKumarKeshri/Hand-Written-Digit-recognition

Implemented feed forward neural network with backpropagation to recognize the digit in each image

ManishKumarKeshri/Improvise-a-Jazz-Solo-with-an-LSTM-Network

Built a model uses Keras, a deep learning library, to generate jazz music. Specifically, it builds an LSTM network, learning from the given MIDI file. It uses deep learning to make music -- something that's considered as deeply human.

ManishKumarKeshri/Keywords-Generater

ManishKumarKeshri/ManishKumarKeshri.github.io

ManishKumarKeshri/MISSIONARY-AND-CANNIBAL-SOLVER

There are 15 Missionary, 15 Cannibal on left side of the river. There is a boat which can carry 6 people at most. The goal is to safely transfer all the missionary and cannibals to the right side of the river. By safely we mean that at any instant like the left side, right side or even on the boat the no. of cannibal should not be greater than the no. of cannibals

ManishKumarKeshri/natural-language-processing

Resources for "Natural Language Processing" Coursera course.

ManishKumarKeshri/NLP-system-to-emojify-a-sentence-using-RNN-LSTM-and-GloVe-Word-Embeddings

Built an NLP system using RNN with LSTM units to assign an emoji to a sentence. Used 50-dimensional GloVe Word Embedding as the features for sentence words and emoji's were assigned using 5 directional SoftMax output.

ManishKumarKeshri/pravega-operator

Pravega Kubernetes Operator

ManishKumarKeshri/pravega-samples

Sample Applications for Pravega.

ManishKumarKeshri/pravega-tools

Tooling for Pravega

ManishKumarKeshri/Resume

ManishKumarKeshri/sample_data

ManishKumarKeshri/tensorflow

Computation using data flow graphs for scalable machine learning

ManishKumarKeshri/the-incredible-pytorch

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.