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Deep Learning : Convolutional Neural Networks with Python

سرفصل های دوره

CNN for Computer Vision and Deep Learning for Segmentation, Object Detection, Classification, Pose Estimation in Python


1. Introduction to Course
  • 1. Introduction

  • 2. Artificial Neurons - The building blocks of Deep Learning
  • 1. Introduction to Deep Learning and Artificial Neurons

  • 3. Introduction to Convolutional Neural Networks (CNNs)
  • 1. Introduction to Convolutional Neural Networks (CNNs)

  • 4. Google Colab Environment Set-up for Writing Python Code
  • 1. Google Colab Environment for Writing Python and Pytorch Code

  • 5. Convolutional Neural Networks from Scratch using Python
  • 1. Define Convolutional Neural Network Architecture from Scratch using Python

  • 6. Dataset and its Augmentation
  • 1. Dataset and its Augmentation

  • 7. Hyperparameters Optimization For Convolutional Neural Networks
  • 1. Hyperparameters Optimization For Training Models

  • 8. Training Convolutional Neural Network from Scratch
  • 1. Training Convolutional Neural Network from Scratch

  • 9. Validating Convolutional Neural Network on Test Images
  • 1. Validating Convolutional Neural Network on Test Images

  • 10. Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs
  • 1. Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

  • 11. Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score
  • 1. Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

  • 12. Resources Python Code for Convolutional Neural Networks from Scratch
  • 1.1 cnn from scratch code.zip
  • 1. Resources Python Code for Convolutional Neural Networks from Scratch.html

  • 13. Pretrained Convolutional Neural Networks
  • 1. Pretrained Convolutional Neural Networks with Python
  • 2.1 Multi Label Classification.zip
  • 2.2 Resources Single Label Classification.zip
  • 2. Python Code to use the Pretrained CNN Models.html

  • 14. Transfer Learning using Convolutional Neural Networks
  • 1. What is Transfer Learning
  • 2. Transfer Learning by Fine Tuning CNNs Models
  • 3. Transfer Learning with CNNs Models as Fixed Feature Extractor
  • 4.1 Code for Transfer Learning by FineTuning and Model Feature Extractor.zip
  • 4.2 Dataset.zip
  • 4. Transfer Learning Python, Pytorch Code and Dataset.html

  • 15. Convolutional Neural Networks Encoder Decoder Architectures
  • 1. Convolutional Neural Networks Based Encoders
  • 2. Convolutional Neural Networks Based Decoders
  • 3. Multi-Task Contextual Encoder Decoder Network

  • 16. YOLO Convolutional Neural Networks
  • 1. YOLO Convolutional Neural Networks Architecture
  • 2. How YOLO Works to Identify Objects

  • 17. Region-based Convolutional Neural Networks
  • 1. Region-based Convolutional Neural Networks (RCNN, FAST RCNN, FASTER RCNN)
  • 2. Detectron2 for Ojbect Detection with PyTorch
  • 3. Perform Object Detection using Detectron2 Models
  • 4.1 Python and PyTorch Code for Object Detection using Detectron2.zip
  • 4. Resources Python and PyTorch Code for Object Detection.html
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    شناسه: 37212
    حجم: 2046 مگابایت
    مدت زمان: 254 دقیقه
    تاریخ انتشار: ۱۶ خرداد ۱۴۰۳
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