sujitjean Goto Github PK

new-space-research-technologies-

Word_cod

new-yourk-taxi-predixtion

nmt

TensorFlow Neural Machine Translation Tutorial

p2-trafficsigns

98.8 solution for german sign classifing problem, done as p2 for Udacity's Self-driving car nano degree

p5_vehicledetection_unet

p5_VehicleDetection_Unet

personalised-cancer-care

production-level-deep-learning

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

python-for-algorithms--data-structures--and-interviews

Files for Udemy Course on Algorithms and Data Structures

quora-case-study

random-forrest-and-gbdt-implementation

Apply both Random Forrest and GBDT on these feature sets Set 1: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(BOW) + preprocessed_eassay (BOW) Set 2: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(TFIDF)+ preprocessed_eassay (TFIDF) Set 3: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(AVG W2V)+ preprocessed_eassay (AVG W2V). Here for this set take 20K datapoints only. Set 4: categorical(instead of one hot encoding, try response coding: use probability values), numerical features + project_title(TFIDF W2V)+ preprocessed_eassay (TFIDF W2V). Here for this set take 20K datapoints only. The hyper paramter tuning (Consider any two hyper parameters preferably n_estimators, max_depth) Consider the following range for hyperparameters n_estimators = [10, 50, 100, 150, 200, 300, 500, 1000], max_depth = [2, 3, 4, 5, 6, 7, 8, 9, 10] Find the best hyper parameter which will give the maximum AUC value Find the best hyper paramter using simple cross validation data You can write your own for loops to do this task Representation of results You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure with X-axis as n_estimators, Y-axis as max_depth, and Z-axis as AUC Score , we have given the notebook which explains how to plot this 3d plot, you can find it in the same drive 3d_scatter_plot.ipynb or You need to plot the performance of model both on train data and cross validation data for each hyper parameter, like shown in the figure seaborn heat maps with rows as n_estimators, columns as max_depth, and values inside the cell representing AUC Score You can choose either of the plotting techniques: 3d plot or heat map Once after you found the best hyper parameter, you need to train your model with it, and find the AUC on test data and plot the ROC curve on both train and test. Along with plotting ROC curve, you need to print the confusion matrix with predicted and original labels of test data points Conclusion You need to summarize the results at the end of the notebook, summarize it in the table format. To print out a table please refer to this prettytable library link

self-driving-cars

Self-Driving-Car Problem Definition We are here building a minimal version of self driving car. Here, we have a front camera view. This will transfer input to the computer. Then Deep Learning algorithm in computer predicts the steering angle to avoid all sorts of collisions. Predicting steering angle can be thought of as a regression problem. We will feed images to Convolutional Neural Network and the label will be the steering angle in that image. Model will learn the steering angle from the as per the turns in the image and will finally predicts steering angle for unknown images. How to Run Just download the repository then run "Self-Driving-Car.ipynb" file for training the model. You can directly restore the pre-trained model from "Saver" folder and predict the output or fine-tune the model further. Dataset Refer this: https://github.com/SullyChen/Autopilot-TensorFlow There are total 45406 images in the dataset along with their steering angles. We will split the dataset into train and test in a ratio of 80:20 sequentially. Objective Our objective is to predict the correct steering angle from the given test image of the road. Here, our loss is Mean Squared Error(MSE). Our goal is to reduce the MSE error as low as possible. Prerequisites You need to have installed following softwares and libraries in your machine before running this project. Python 3: https://www.python.org/downloads/ Anaconda: It will install ipython notebook and most of the libraries which are needed like pandas, matplotlib, numpy and scipy: https://www.anaconda.com/download/ Libraries: Tensorflow: It is a deep learning library. pip install tensorflow OpenCV: It is used for processing images. pip install opencv-python

ssd_keras

A Keras port of Single Shot MultiBox Detector

stack-overflow

stochastic-gradient-descent-sgd-custom-implementation

in this project i have tried and implementated the Stochastic Gradient Descent (SGD) classifier in python

sudoku-gui-solver

This is a sudoku solver using the backtracking algorithm. It includes a graphical GUI as well as a text based version.

sujit

support_vector_machine

svd-and-custom-word-vector-

Here I am using the Matrix Factorization technique to form Custom word vector . For implementation of all the code I have used the SKlearn Library. step 1 Select the top 2k words from essay text and project_title (concatinate essay text with project title and then find the top 2k words) based on their idf_ values step 2 Compute the co-occurrence matrix with these 2k words, with window size=5 (ref) step 3 Use TruncatedSVD on calculated co-occurance matrix and reduce its dimensions, choose the number of components (n_components) using elbow method The shape of the matrix after TruncatedSVD will be 2000*n, i.e. each row represents a vector form of the corresponding word. Vectorize the essay text and project titles using these word vectors. (while vectorizing, do ignore all the words which are not in top 2k words) step 4 Concatenate these truncatedSVD matrix, with the matrix with features school_state : categorical data clean_categories : categorical data clean_subcategories : categorical data project_grade_category :categorical data teacher_prefix : categorical data quantity : numerical data teacher_number_of_previously_posted_projects : numerical data price : numerical data sentiment score's of each of the essay : numerical data number of words in the title : numerical data number of words in the combine essays : numerical data word vectors calculated in step 3 : numerical data step 5: Apply GBDT on matrix that was formed in step 4 of this assignment, DO REFER THIS BLOG: XGBOOST DMATRIX step 6:Hyper parameter tuning (Consider any two hyper parameters) Find the best hyper parameter which will give the maximum AUC value Find the best hyper paramter using k-fold cross validation or simple cross validation data Use gridsearch cv or randomsearch cv or you can also write your own for loops to do this task of hyperparameter tuning

talos

Hyperparameter Optimization for Keras Models

udacity-data-analyst-nanodegree