Deep Learning with Python & Pytorch for Image Classification
What you'll learn
- Learn Image Classification using Deep Learning PreTrained Models with Python and PyTorch
- Learn Single-Label Image Classification and Multi-Label Image Classification with Python and PyTorch
- Learn Learn Convolutional Neural Networks (CNN) including LeNet, AlexNet, Resnet, GoogleNet, VGG
- Learn Deep Learning Architectures Such as ResNet and AlexNet to Perform the Image Classification with PyTorch and Python
- Write Python and Pytroch Code in Google Colab
- Perform Data Preprocessing using Transformations
- Perform Single-Label Image Classification with ResNet and AlexNet
- Perform Multi-Label Image Classification with ResNet and AlexNet
- Learn Transfer Learning
- Dataset, Data Augmentation, Dataloaders, and Training Function
- Deep ResNet Model FineTuning
- ResNet Model HyperParameteres Optimization
- Deep ResNet as Fixed Feature Extractor
- Models Optimization, Training and Results Visualization
Requirements
- Deep Learning with Python and Pytorch is taught in this course
- A Google Gmail account to get started with Google Colab to write Python Code
Description
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 identify objects with incredible accuracy? If so, then our 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.
You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.
You will learn how to connect Google Colab with Google Drive and how to access data.
You will perform data preprocessing using different transformations such as image resize and center crop etc.
You will perform two types of Image Classification, single-label Classification, and multi-label Classification using deep learning models with Python.
Learn Convolutional Neural Networks (CNN) including LeNet, AlexNet, Resnet, GoogleNet, VGG
You will be able to learn Transfer Learning techniques:
1. Transfer Learning by FineTuning the model.
2. Transfer Learning by using the Model as Fixed Feature Extractor.
You will learn how to perform Data Augmentation.
You will learn how to load Dataset, Dataloaders.
You will Learn to FineTune the Deep Resnet Model.
You will learn how to use the Deep Resnet Model as Fixed Feature Extractor.
You will Learn HyperParameters Optimization and results visualization.
In single-label Classification, when you feed input image to the network it predicts single label. In multi-label Classification, when you feed input image to the network it predicts multiple labels. You will Learn Deep Learning architectures such as ResNet and AlexNet. The ResNet is a deep convolution neural network proposed for image classification and recognition. ResNet network architecture designed for classification task, trained on the imageNet dataset of natural scenes that consists of 1000 classes. Deep residual nets won the 1st place on the ILSVRC 2015 Classification challenge. Alexnet is a deep convolution neural network trained on ImageNet dataset to classify the images into 1000 classes. It has five convolution layers followed by max-pooling layers, and 3 fully connected layers. AlexNet won the ILSVRC 2012 Classification challenge. You will perform image classification using ResNet and AlexNet deep learning models. The Deep Learning community has greatly benefitted from these open-source models where pre-trained models are a major reason for rapid advancements in the Computer Vision and deep learning research.
Who this course is for:
- Deep Learning enthusiasts interested to learn with Python and Pytorch
- Students and researchers interested in Deep Learning for Image Classification
Instructors
- 4.3 Instructor Rating
- 332 Reviews
- 9,705 Students
- 6 Courses
AI & Computer Science School aims to equip you with the skills and knowledge necessary to succeed in today's technology industry. We offer a wide range of cutting-edge computer science courses that cover a range of subjects, including Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Data Science (DS), Programming, and Databases. Our course material is designed to accommodate students of all levels, from beginner to advanced.
By taking our courses, you will gain a deeper understanding of the latest technologies and commercial applications of computing practices. You'll learn how to solve complex problems and develop your technical and transferable skills through hands-on exercises and project-based learning. Our courses are taught by experienced industry professionals who are passionate about teaching and dedicated to helping you succeed.
With AI & Computer Science School, you'll have the opportunity to develop in-demand computing skills and improve your marketability and competitiveness in the tech industry. Our focus on practical, hands-on learning will help you gain real-world experience and hone your innovation and creativity. Whether you're looking to advance your career, transition into a new field, or simply explore your passion for technology, we have the resources and support you need to achieve your goals.
See you inside the courses!
- 4.3 Instructor Rating
- 462 Reviews
- 13,034 Students
- 7 Courses
Mazhar Hussain is currently in the role of Deep Learning and Computer Vision Engineer. He has extensive teaching experience at University Higher Education level and Online over a decade. He has published several research papers on Deep Learning in well-reputed Journals and Conferences. He believes on comprehensive practical trainings 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 is teaching Computer Science courses at the National University of Computer and Emerging Sciences and Online since 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
He holds a Master's Degree in Computer Science and is passionate to deliver practical knowledge and skills to his students. He has worked as a developer in the Microsoft Innovation Center and is now taking all that he has learned to help you discover amazing career opportunities.
Mazhar believes that courses should teach real life skills that are current and they should not waste a student's valuable time. His courses are the most comprehensive and well-explained option available out there. One must start with the foundation and build upon it to learn effectively. He promises that His approach allows for exponential learning.
He believes that everyone has the potential to learn and excel, and He is dedicated to helping his students achieve their full potential. He is excited to share his knowledge and experience with you, and look forward to helping you achieve your goals.
See you inside the courses!
Please do not hesitate if you have any questions, He is always available for your help at any time to transform a passionate, enthusiastic learner into a skilled person.