I am currently working at Abeja as Deep Learning Researcher and interested in Applied Deep Learning.
Most frequently used tools are : Pytorch, Keras, Tensorflow, Nvidia-Docker, Opencv, Scikit-Learn
Themes that I have worked on :
- Object Detection, Image Segmentation and Classification problems with industrial setting.
- Generative Adversarial Nets(GANs) and Auto Encoder Modeling for research experimentation
- Visual SLAM for 3D structure estimation and mapping for autonomous robots.
- Pytorch Tutorial for Practitioners
- Densenet in TF
- GAN and Equilibrium
- Boundary Equilibrium GAN
Programming and Tools
I graduated in Information Science as M.Eng from NAIST with thesis in Robotics and Machine Learning. I did my undergraduate in Electrical Engineering from IITJ with projects in video object tracking on embedded platform.
Practical Computer Vision
In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you’ll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you’ll use them to find similar-looking objects. With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You’ll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset.
By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.