Pinned Repositories
.github-workflows
a-2017
Public Repository for cs109a, 2017 edition
Agile_Data_Code_2
Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition
AI-Notebooks
aiven-examples
Aiven "getting started" code examples
amazon-sagemaker-examples
Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker
analytics-integration-samples
Repository contains samples to integrate Watson IoT with different analytics services
anomaly-detection
Anomaly Detection model uses Spark for training and Spark Streaming for testing
anomaly_detection
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Radrangi's Repositories
Radrangi/a-2017
Public Repository for cs109a, 2017 edition
Radrangi/AI-Notebooks
Radrangi/amazon-sagemaker-examples
Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker
Radrangi/awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
Radrangi/Azure
Radrangi/best-heart-drug-selection
Created for toolchain: https://console.bluemix.net/devops/toolchains/74c65cd6-d1b4-444e-92e9-0b065ccbe1d8?env_id=ibm%3Ayp%3Aus-south
Radrangi/dataquest_eng
Here's how to get DataQuest's Data Engineering Track missions' content to work on your localhost. Using data from my Valenbisi ARIMA modeling project, I document my steps using PostgreSQL, Postico, and the Command Line to get our DataQuest exercises running out of a Jupyter Notebook.
Radrangi/deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Radrangi/Deep-Reinforcement-Learning-for-Atari-Games
Train our agent to play Breakout Game using 4 models and 2 CNN structures
Radrangi/devopsinsights-toolchain-20181110153202637
Created for toolchain: https://console.bluemix.net/devops/toolchains/846962e0-7a7b-4170-b6e1-a716e869ef23?env_id=ibm%3Ayp%3Aus-south
Radrangi/fast-data-dev
Kafka Docker for development. Kafka, Zookeeper, Schema Registry, Kafka-Connect, Landoop Tools, 20+ connectors
Radrangi/image-classification-using-cnn-and-keras
Classify images, specifically document images like ID cards, application forms, and cheque leafs, using CNN and the Keras libraries.
Radrangi/image-recognition-and-information-extraction-from-image-documents
Image Recognition and Information Extraction from Image Documents using Keras and Watson NLU
Radrangi/jupyter
Jupyter metapackage for installation, docs and chat
Radrangi/jupyterlab-github
GitHub integration for JupyterLab
Radrangi/ML-Notebooks
Radrangi/ML-Notebooks2
Radrangi/mlflow
Open source platform for the machine learning lifecycle
Radrangi/movie-recommender-demo
This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).
Radrangi/Mutual-Fund-Performance-Prediction
Radrangi/pretrained.ml
[DEPRECATED] Compilation of pre-trained deep learning models with demos and code.
Radrangi/product-line-prediction
Created for toolchain: https://console.bluemix.net/devops/toolchains/a6d21098-06f1-46c0-88e7-8421a2d39552?env_id=ibm%3Ayp%3Aus-south
Radrangi/pydoop
A Python MapReduce and HDFS API for Hadoop
Radrangi/pygdf
Python GPU DataFrame Library
Radrangi/pyspider
A Powerful Spider(Web Crawler) System in Python.
Radrangi/Real-Time-Multiple-Object-Detection
The ability of the computer to locate and identify each object in an image/video is known as object detection. Object detection has many applications in self-driving cars, pedestrian counting, face detection, vehicle detection etc. One of the crucial element of the self-driving car is the detection of various objects on the road like traffic signals, pedestrian’s other vehicles, sign boards etc. In this project, Convolutional Neural Network (CNN) based approach is used for real-time detection of multiple objects on the road. YOLO (You Only Look Once) v2 Deep Learning model is trained on PASCAL VOC dataset. We achieved mAP score of 78 on test dataset after training the model on NVIDIA DGX-1 V100 Super Computer. The trained model is then applied on recorded videos and on live streaming received through web cam.
Radrangi/Simple-Perceptron-Network
Building simple neural networks from scratch using Perceptrons
Radrangi/spark
Mirror of Apache Spark
Radrangi/sparkler
Spark-Crawler : Evolving Apache Nutch to run on Spark.
Radrangi/Stock-Prediction