Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set. With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management. In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights. Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

1 minute read

Deep Learning for Computer Vision

Deep Learning – Computer Vision

Foundation of Computer Vision

  1. Common Architectural Principles of Deep Networks
  2. Building Blocks of Deep Networks
  3. Convolutional Neural Networks (CNNs)
  4. Recurrent Neural Networks
  5. Recursive Neural Networks; Applications to Sequence Data
  6. Anomaly Detection
  7. Tuning Deep Networks
  8. Vectorization
  9. Data Mining (Pre-requisites)

CNN overview

  1. CNN Definition
  2. CNN based Architectures
  3. End to end CNN network
  4. Training CNN
  5. Deployment in Azure Cloud
  6. Performance tuning of CNN network

Advance Computer Vision – Part 1

  1. CNN Architectures with research paper and mathematics
  2. Resnet-5 variants with research paper and practical
  3. AlexNet variants with research paper and practical
  4. GoogleNet variants with research paper and practical
  5. Transfer learning
  6. VGGNet variants with research paper and practical
  7. Inception net variants with research paper and practical
  8. Darknet variants with research paper and practical

Advance Computer Vision – Part 2

  1. Object detection in-depth
  2. Transfer learning
  3. RCNN with research paper and practical
  4. Fast RCNN with research paper and practical
  5. Faster RCNN with research paper and practical
  6. SSD with research paper and practical
  7. SSD lite with research paper and practical

Training of Custom Object Detection

  1. TFOD introduction
  2. Environment setup wtih TFOD
  3. GPU vs TPU vs CPU
  4. GPU Comparison

Advance Computer Vision – Part 3

  1. Yolo v1 with research paper and practical
  2. Retina net
  3. Face net
  4. Detectron2 with practical and live testing

Object segmentation

  1. Semantic segmentation
  2. Panoptic segmentation
  3. Masked RCNN
  4. Practical with Detectron
  5. Practical with TFOD

Object tracking

  1. Detail of object tracking
  2. Kalman filtering
  3. Sort
  4. Deep sort
  5. Object tracking live project with live camera testing

OCR

  1. Introduction to OCR
  2. Various framework and API for OCR
  3. Practical implementation of OCR
  4. Live project deployment for bill parsing

Image captioning

  1. Image captioning overview
  2. Image captioning project with deployment

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