Dipali290's starred repositories
RNN_LSTM-on-Energy-Data-
RNN_LSTM Implementation on open source energy data using Python 3.0 with Tensorflow back end
ML-based-Energy-Consumption-Prediction
The code is an LSTM Recurrent Neural Network that predicts energy consumption based on the usage over the past 60 days.
BayesianLSTM
Bayesian LSTM Implementation in PyTorch
forecasting-energy-consumption-LSTM
Development of a machine learning application for IoT platform to predict electric energy consumption in smart building environment in real time.
Andrew-NG-Notes
This is Andrew NG Coursera Handwritten Notes.
Sparse_Pooling
SHPL: Fusing Bird's Eye View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection
3d-object-tracking-lidar-camera
3D Object Tracking with time-to-collision (TTC) estimation using Camera and Lidar for collision avoidance system in autonomous vehicles.
Sensor-Fusion-Lidar-Object-Detection
Using Euclidiean Clustering and RANSAC to detect Objects in Lidar captured Point Clouds (PCDs)
ADAS-Car-using-Raspberry-Pi
ADAS Car - with Collision Avoidance System (CAS) - on Indian Roads using LIDAR-Camera Low-Level Sensor Fusion. DIY Gadget built with Raspberry Pi, RP LIDAR A1, Pi Cam V2, LED SHIM, NCS 2 and accessories like speaker, power bank etc
AD_Sensor_Fusion
Sensor Fusion of Perception sensors ( Camera, Lidar and Radar) in Autonomous Driving
multi-task-refinenet
Multi-Task (Joint Segmentation / Depth / Surface Normas) Real-Time Light-Weight RefineNet
VGG16-Pretrained-C
Pretrained VGG16 neural net in C language
Depth-VO-Feat
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
deepschool.io
Deep Learning tutorials in jupyter notebooks.
CNNLocalizationTF
server side code for CNN localization
deep-speed-constrained-ins
Codes for Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
ACRV-SLAM_using_DL
Depth and pose estimation using ConvNets
face-api.js
JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
FeatherNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
Code for 3rd Place Solution in Face Anti-spoofing Attack Detection Challenge @ CVPR2019,model only 0.35M!!! 1.88ms(CPU)