Pinned Repositories
_Assignment1
Show json data in table.
AED_INFO5100
Northeastern University
Banana_Navigation_Unity_ML-Agents
Project Overview For this project, you will train an agent to navigate (and collect bananas!) in a large, square world. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of your agent is to collect as many yellow bananas as possible while avoiding blue bananas. The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around the agent's forward direction. Given this information, the agent has to learn how to best select actions. Four discrete actions are available, corresponding to: 0 - move forward. 1 - move backward. 2 - turn left. 3 - turn right. The task is episodic, and in order to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes. The Environment Follow the instructions below to explore the environment on your own machine! You will also learn how to use the Python API to control your agent. Step 1: Clone the DRLND Repository If you haven't already, please follow the instructions in the DRLND GitHub repository to set up your Python environment. These instructions can be found in README.md at the root of the repository. https://github.com/udacity/deep-reinforcement-learning#dependencies By following these instructions, you will install PyTorch, the ML-Agents toolkit, and a few more Python packages required to complete the project. Step 2: Download the Unity Environment For this project, you will not need to install Unity - this is because we have already built the environment for you, and you can download it from one of the links below. You need only select the environment that matches your operating system: Linux: click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux.zip Mac OSX: click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana.app.zip Windows (32-bit): click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Windows_x86.zip Windows (64-bit): click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Windows_x86_64.zip Then, place the file in the p1_navigation/ folder in the DRLND GitHub repository, and unzip (or decompress) the file. Step 3: Explore the Environment After you have followed the instructions above, open Navigation.ipynb (located in the p1_navigation/ folder in the DRLND GitHub repository) and follow the instructions to learn how to use the Python API to control the agent.
Car_price_predictor_Using_ML
Simple old car price predictor using ML and a data-set provided by Kaggle. Here we used mainly knn, Decision tree regression and Gradient Boosting as our main algo and scikit learn python library for the same.
CharityML
In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. The dataset for this project originates from the UCI Machine Learning Repository. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". You can find the article by Ron Kohavi online. The data we investigate here consists of small changes to the original dataset, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries.
cnc_farmbot_project
Continuous_Control_DDPG
Convex-Hull
solution of convex hull problem using jarvis march algorithm
Language_converter_python_script
convert English text file to any language text file using python and googletrans/Translator library
ReactCalculator
Basic calculator with all functionalities made in react js and appearance is kept close to google calculator
Harshal-Jaiswal's Repositories
Harshal-Jaiswal/Banana_Navigation_Unity_ML-Agents
Project Overview For this project, you will train an agent to navigate (and collect bananas!) in a large, square world. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of your agent is to collect as many yellow bananas as possible while avoiding blue bananas. The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around the agent's forward direction. Given this information, the agent has to learn how to best select actions. Four discrete actions are available, corresponding to: 0 - move forward. 1 - move backward. 2 - turn left. 3 - turn right. The task is episodic, and in order to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes. The Environment Follow the instructions below to explore the environment on your own machine! You will also learn how to use the Python API to control your agent. Step 1: Clone the DRLND Repository If you haven't already, please follow the instructions in the DRLND GitHub repository to set up your Python environment. These instructions can be found in README.md at the root of the repository. https://github.com/udacity/deep-reinforcement-learning#dependencies By following these instructions, you will install PyTorch, the ML-Agents toolkit, and a few more Python packages required to complete the project. Step 2: Download the Unity Environment For this project, you will not need to install Unity - this is because we have already built the environment for you, and you can download it from one of the links below. You need only select the environment that matches your operating system: Linux: click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux.zip Mac OSX: click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana.app.zip Windows (32-bit): click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Windows_x86.zip Windows (64-bit): click here https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Windows_x86_64.zip Then, place the file in the p1_navigation/ folder in the DRLND GitHub repository, and unzip (or decompress) the file. Step 3: Explore the Environment After you have followed the instructions above, open Navigation.ipynb (located in the p1_navigation/ folder in the DRLND GitHub repository) and follow the instructions to learn how to use the Python API to control the agent.
Harshal-Jaiswal/Language_converter_python_script
convert English text file to any language text file using python and googletrans/Translator library
Harshal-Jaiswal/ReactCalculator
Basic calculator with all functionalities made in react js and appearance is kept close to google calculator
Harshal-Jaiswal/_Assignment1
Show json data in table.
Harshal-Jaiswal/AED_INFO5100
Northeastern University
Harshal-Jaiswal/Car_price_predictor_Using_ML
Simple old car price predictor using ML and a data-set provided by Kaggle. Here we used mainly knn, Decision tree regression and Gradient Boosting as our main algo and scikit learn python library for the same.
Harshal-Jaiswal/CharityML
In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. The dataset for this project originates from the UCI Machine Learning Repository. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". You can find the article by Ron Kohavi online. The data we investigate here consists of small changes to the original dataset, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries.
Harshal-Jaiswal/cnc_farmbot_project
Harshal-Jaiswal/Continuous_Control_DDPG
Harshal-Jaiswal/Convex-Hull
solution of convex hull problem using jarvis march algorithm
Harshal-Jaiswal/Customer_segments_unsupervised_learning
Perform an unsupervised learning to identify costumer segments. Data cannot be provided as per Udacity's terms and conditions.
Harshal-Jaiswal/Disease_Prediction
Harshal-Jaiswal/File_iterator_and_finder_python
iterates through every file in a folder and can find some string needed and can also replace it.
Harshal-Jaiswal/Flutter_basics
flutter basics
Harshal-Jaiswal/Image_Classification_using_Deep_Learning
In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset from Oxford of 102 flower categories, you can see a few examples below.
Harshal-Jaiswal/INFO6205_PSA
Fall 2021, sem 1, Programming Structure & Algorithms.
Harshal-Jaiswal/Keras_Learning_udemy
practical-deep-learning-with-keras
Harshal-Jaiswal/Lamport-and-vector-clock
lamport vector clock code java
Harshal-Jaiswal/React_Basic_News_App
Bacic news app made with react js library.
Harshal-Jaiswal/React_Maps
Harshal-Jaiswal/React_Native_Basic
basic react native app
Harshal-Jaiswal/React_Native_Firebase
React native firebase functionality like auth, admob, etc
Harshal-Jaiswal/React_Native_map_tracing
Harshal-Jaiswal/React_nba_app
Harshal-Jaiswal/React_Weather_App
Advance weather app that provides functionality to add various cities simultaneously and view current weather at that location. This project is created with the help of react js and states are maintained with redux.
Harshal-Jaiswal/ReactNativeProject
Basic trial on React-Native projects
Harshal-Jaiswal/Tennis_Unity_ML_MADDPG-
Harshal-Jaiswal/TheStackGame
TechGig coding problem 'The Stack Game' solution using recursion.
Harshal-Jaiswal/Time_controlled_switch
arduino based time control switch to on and off the electrical applience based on time
Harshal-Jaiswal/Yocket_Data_Analysis
Basic data scrapping from yocket website and its analysis such as gre and toefl score for admits of colleges. With login feature to access the feature which works with login.