The SQL & Database Certification Training Bundle
What's Included
Deep Learning with Python for Image Classification
- Experience level required: Beginner
- Access 25 lectures & 2 hours of content 24/7
- Length of time users can access this course: Lifetime
Course Curriculum
25 Lessons (2h)
Your First Program
Section 1 - Introduction to the Course
Introduction to the Course2:22Section 2 - Define Image Classification
Image Classification with single label and multi-label2:59Section 3 - Pretrained Models Definition
PreTrained Models and their Applications5:11Section 4 - Deep Learning Architectures for Image Classification
Deep Learning ResNet and AlexNet Architectures for Image Classification4:58Section 5 - Google Colab for Writing Python Code
Set-up Google Colab for Writing Python Code5:11Section 6 - Connect Google Colab with Google Drive
Connect Google Colab with Google Drive to Read and Write Data2:43Section 7 - Access Data from Google Drive to Colab
Read Data from Google Drive to Colab Notebook2:44Section 8 - Data Preprocessing for Image Classification
Lecture 1 - Perform Data Preprocessing for Image Classification5:00Section 9 - Single-Label Image Classification using Deep Learning Models
Single-Label Image Classification using ResNet and AlexNet PreTrained Models8:03Resources Single_Label Classification (Python Code)Section 10 - Multi-Label Image Classification using Deep Learning Models
Lecture 2 - Resources Multi_Label ClassificationMulti-Label Image Classification using ResNet and AlexNet PreTrained Models6:21Section 11- Transfer Learning
Introduction to Transfer Learning6:10Section 12- Link Google Drive with Google Colab
Link Google Drive with Google Colab2:43Section 13 - Dataset, Data Augmentation, Dataloaders, and Training Function
Dataset, Data Augmentation, Dataloaders, and Training Function7:20Section 14 - Deep ResNet Model FineTuning
Deep ResNet Model FineTuning7:19Section 15 - Model Optimization
ResNet Model HyperParameteres Optimization6:22Section 16 -Deep ResNet Training
Deep ResNet Model Training3:34Section 17 - Deep ResNet Feature Extractor
Deep ResNet as Fixed Feature Extractor4:42Section 18 - Model Optimization, Training and Results
Model Optimization, Training and Results Visualization5:51Section 19 - Resources Code for Transfer Learning by FineTuning and Model Feature Extractor
Code of Classification using Transfer Learning1:41Code for Transfer Learning by FineTuning and Model Feature ExtractorSTARTClassification Dataset
Deep Learning with Python for Image Classification
Mazhar Hussain | CS Lecturer | Deep Learning , AI, CV & Data Engineer
Mazhar Hussain is currently in the role of Deep Learning and Computer Vision Engineer. He has over a decade of teaching experience at the University Higher Education level and Online. He has published several research papers on Deep Learning in well-reputed Journals and Conferences. He believes in comprehensive practical training with stunning support for his students where all his courses are 100% hands-on with step-by-step problem-based learning, demos, and examples.
Mazhar Hussain has been teaching Computer Science courses at the National University of Computer and Emerging Sciences and Online for a decade. He has been teaching courses in:
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Deep Learning (DL)
- Computer Vision (CV)
- Data Science (DS)
- Programming (Python, C++, Java)
- Databases especially in SQL SERVER, MYSQL, ORACLE, and MS ACCESS
Description
Learn Deep Learning, Machine Learning & Computer Vision for Image Classification in PyTorch Using CNN Transfer Learning
Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can accurately identify objects? If so, then this course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques.
What you'll learn
- Access 25 lectures & 2 hours of content 24/7
- Use Google Colab notebooks for writing the Python code for image classification using Deep Learning models
- Learn how to connect Google Colab with Google Drive & how to access data
- Perform data preprocessing using different transformations such as image resize & center crop etc.
- Perform two types of Image Classification, single-label & multi-label classifications using deep learning models with Python
- Learn Transfer Learning techniques
- Transfer Learning by Fine-Tuning the model
- Transfer Learning by using the Model as Fixed Feature Extractor
- Learn how to perform Data Augmentation
- Load Dataset, Dataloaders
- Learn to fine-tune the Deep ResNet Model
- Use the Deep Resnet Model as Fixed Feature Extractor
- Learn HyperParameters Optimization & results visualization
Specs
Important Details
- Length of time users can access this course: lifetime
- Access options: desktop & mobile
- Redemption deadline: redeem your code within 30 days of purchase
- Experience level required: beginner
- Certificate of Completion ONLY
- Have questions on how digital purchases work? Learn more here
Requirements
- Any device with basic specifications
- A Google Gmail account to get started with Google Colab to write Python Code
Mastering Microsoft Access Database: Hands-On Training with Exercises
SQL DATABASE Queries Hands-On Training with MS SQL SERVER
Deep Learning for Image Segmentation with Python & PyTorch
Terms
- Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.