Deepak Sridhar's repositories
Deep-Learning-Coursera
This repository contains the programming assignments for Deep Learning specialization courses by Andrew Ng. It deals with the following concepts. DNNs, Hyperparameter tuning, Regularization, Optimization, CNNs (LeNet5, AlexNet, VGG, ResNet, Inception Network), Transfer Learning (Neural Style Transfer), RNNs (LSTM, GRU) and Structuring Machine Learning Projects.
ObjectDetectionApp
This Android App recognizes Cats/Dogs from a picture using Deep Conv Nets fine-tuned from VGG-16 model and Happy faces using custom deep convolutional neural networks in Keras.
Machine-Learning-Coursera
This repository contains the programming assignments for the Machine Learning course by Andrew Ng. It deals with the following concepts. Linear Regression, Logistic Regression, Neural Networks, Bias and Variance, SVM, PCA , K-means Clustering, Anomaly Detection and Recommender Systems.
Bluetooth_tracker
This app allows you to track bluetooth devices from your smartphone and raises an alarm if it is out of range of the bluetooth
ds_ltv_deep_learning
This repository contains the files for training a linear time-varying system to identify the nature of the parameters using Deep Learning concepts.
android-interview-questions
Your Cheat Sheet For Android Interview - Android Interview Questions
AutonomousDrivingCookbook
Scenarios, tutorials and demos for Autonomous Driving
awesome-deep-learning-papers
The most cited deep learning papers
DigitDetection
This Android App recognizes Digits (0-9) from a picture using my CNN Keras model for MNIST dataset.
cameraview
Easily integrate Camera features into your Android app
Deep-Learning-Udacity
This repository contains the programming assignments for Deep Learning course by Udacity
deeplearning-finetune
This repository contains code on finetuning existing models (from fast.ai)
mlcourse_open
OpenDataScience Machine Learning course. Launches on Feb, 5 both in English and Russian
SSD-Tensorflow
Single Shot MultiBox Detector in TensorFlow
tensorflow
Computation using data flow graphs for scalable machine learning