Deep Learning : Convolutional Neural NetWorks With Python

Deep Learning : Convolutional Neural Networks With Python
Published 3/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

What you’ll learn

Deep Convolutional Neural Networks with Python and Pytorch Basics to Expert

Define Convolutional Neural Network Architecture from Scratch with Python and Pytorch

Hyperparameters Optimization For Convolutional Neural Networks to Improve Model Performance

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

Python Programming experience is an advantage but not required

Description

Overview

Section 1: Introduction to Course

Lecture 1 Introduction

Section 3: Introduction to Convolutional Neural Networks (CNNs)

Lecture 3 Introduction to Convolutional Neural Networks (CNNs)

Section 4: Google Colab Environment Set-up for Writing Python Code

Lecture 4 Google Colab Environment for Writing Python and Pytorch Code

Section 5: Convolutional Neural Networks from Scratch using Python

Lecture 5 Define Convolutional Neural Network Architecture from Scratch using Python

Section 6: Dataset and its Augmentation

Lecture 6 Dataset and its Augmentation

Section 7: Hyperparameters Optimization For Convolutional Neural Networks

Lecture 7 Hyperparameters Optimization For Training Models

Section 8: Training Convolutional Neural Network from Scratch

Lecture 8 Training Convolutional Neural Network from Scratch

Section 9: Validating Convolutional Neural Network on Test Images

Lecture 9 Validating Convolutional Neural Network on Test Images

Section 10: Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Lecture 10 Performance Metrics (Accuracy, Precision, Recall, F1 Score) to Evaluate CNNs

Section 11: Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Lecture 11 Visualize Confusion Matrix and Calculate Precision, Recall, and F1 Score

Section 12: Resources: Python Code for Convolutional Neural Networks from Scratch

Lecture 12 Resources: Python Code for Convolutional Neural Networks from Scratch

Section 13: Pretrained Convolutional Neural Networks

Lecture 13 Pretrained Convolutional Neural Networks with Python

Lecture 14 Python Code to use the Pretrained CNN Models

Section 14: Transfer Learning using Convolutional Neural Networks

Lecture 15 What is Transfer Learning

Lecture 16 Transfer Learning by Fine Tuning CNNs Models

Lecture 17 Transfer Learning with CNNs Models as Fixed Feature Extractor

Lecture 18 Transfer Learning Python, Pytorch Code and Dataset

Section 15: Convolutional Neural Networks Encoder Decoder Architectures

Lecture 19 Convolutional Neural Networks Based Encoders

Lecture 20 Convolutional Neural Networks Based Decoders

Lecture 21 Multi-Task Contextual Encoder Decoder Network

Section 16: YOLO Convolutional Neural Networks

Lecture 22 YOLO Convolutional Neural Networks Architecture

Lecture 23 How YOLO Works to Identify Objects

Section 17: Region-based Convolutional Neural Networks

Lecture 24 Region-based Convolutional Neural Networks (RCNN, FAST RCNN, FASTER RCNN)

Lecture 25 Detectron2 for Ojbect Detection with PyTorch

Lecture 26 Perform Object Detection using Detectron2 Models

Lecture 27 Resources: Python and PyTorch Code for Object Detection

This course is designed for individuals with a keen interest in Deep Learning and Convolutional Neural Networks (CNNs) with Python and Pytorch to solve Real-World AI Problems.,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.