Deep Learning in Computer Vision

This repo collects the material of Coursat.ai Deep Learning in Computer Vision Class.

Rounds:

Round 1:

• April 2020
• 15 Attendees

Capstone Project:

• MultiCheXNet, paper, code

Lectures:

• Lecture 1: Introduction to Practical AI for Engineers

  • Video
  • Code
  • Python Review
  • IPython and Jupyter

• Lecture 2: Introduction to Deep Learning

  • Video
  • Deep Learning with Python and Keras

• Lecture 3: Building blocks of Neural Nets

  • Video
  • Building blocks of Neural Nets
  • Effect of Batch size

• Lecture 4: Fundamentals of Practical Deep Learning Process

  • Video
  • Conventions of Deep Learning Data and Tensors
  • Example: dealing with Video/Temporal data
    • Video DNN
    • Video CNN
  • Deep Learning Process Fundamentals

• Lecture 5: Introduction to ConvNets

  • Video
  • Basics of ConvNets
  • ConvNets with Small Datasets (Data Augmentation)

• Lecture 6: Transfer Learning and Pre-trained ConvNets

  • Video
  • Transfer Learning with ConvNets
  • Keras Functional Models

• Lecture 7: Semantic Segmentation with ConvNets

  • Video
  • Visualizing ConvNets
  • Semantic Segmentation on CAMVID dataset

• Lecture 8: Object Detection with ConvNets

  • Video
  • Single Object Detection
  • Multiple Label Object Detection
  • Multiple Objects Detection
  • Training Yolo v3 on custom dataset

• Lecture 9: Generative Adversarial Nets (GANs)

  • Part 1 video
  • Part 2 video
  • Auto Encoders
  • GAN - CIFAR10
  • GANs Diagnosis
  • Conditional GANs
  • CycleGAN