Home : Book details : Book description

Description of  Deep Learning Convolutional Neural Networks with Python (2024)

Published 3/2024 Created by Dr. Mazhar Hussain MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 27 Lectures ( 4h 13m ) | Size: 2 GB Deep Learning and Computer Vision using Convolutional Neural Networks with Python, Pytorch. Train, Test, Deploy Models What you'll learn: Deep Convolutional Neural Networks with Python and Pytorch Basics to Expert Introduction to Deep Learning and its Building Blocks Artificial Neurons Define Convolutional Neural Network Architecture from Scratch with Python and Pytorch Hyperparameters Optimization For Convolutional Neural Networks to Improve Model Performance Custom Datasets with Augmentations to Increase Image Data Variability Training and Testing Convolutional Neural Network using Pytorch Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score Advanced CNNs for Segmentation, Object tracking, and Pose Estimation. Pretrained Convolutional Neural Networks and their Applications Transfer Learning using Convolutional Neural Networks Models Convolutional Neural Networks Encoder Decoder Architectures YOLO Convolutional Neural Networks for Computer Vision Tasks Region-based Convolutional Neural Networks for Object Detection Requirements: A Google Gmail account is required to get started with Google Colab to write Python Code Python Programming experience is an advantage but not required Description: Who this course is for: Whether you're a beginner looking to build a strong foundation in Computer Vision, Object Tracking, Segmentation, Pose Estimation, Classification, Object Detection or an experienced professional aiming to enhance your skills, this course provides valuable insights and hands-on experience with CNNs.