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Fashion Image Classification using CNNs in Pytorch
Mohammed Murtuza Qureshi
Mohammed Murtuza Qureshi
In this 1-hour long project-based course, you will learn how to create Neural Networks in the Deep Learning Framework PyTorch. We will creating a Convolutional Neural Network for a 10 Class Image Classification problem which can be extended to more classes. We will start off by looking at how perform data preparation and Augmentation in Pytorch.
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In this 1-hour long project-based course, you will learn how to create Neural Networks in the Deep Learning Framework PyTorch. We will creating a Convolutional Neural Network for a 10 Class Image Classification problem which can be extended to more classes. We will start off by looking at how perform data preparation and Augmentation in Pytorch.
Read more
In this 1-hour long project-based course, you will learn how to create Neural Networks in the Deep Learning Framework PyTorch. We will creating a Convolutional Neural Network for a 10 Class Image Classification problem which can be extended to more classes. We will start off by looking at how perform data preparation and Augmentation in Pytorch.
We will be building a Neural Network in Pytorch. We will add the Convolutional Layers as well as Linear Layers. We will then look at how to add optimizer and train the model. Finally, we will test and evaluate our model on test data.
The project will get you introduced with Pytorch. You will in the end understand how the framework works and get you started with building Neural Networks in Pytorch.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/3sxszl/fashion
What's inside
Syllabus
Project Overview
Welcome to this Guided Project on Fashion Image Classification using CNNs in Pytorch. In this project, you will learn the step by step process of how to train a Convolutional Neural Network in the Deep Learning Framework developed by Facebook, Pytorch. We will start with data preparation and augmentation followed by building the neural network then training the model by defining loss and and finally testing and evaluating the model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches a deep learning framework that is widely used in industry and academia
Provides a practical understanding of creating Neural Networks in PyTorch
Guides learners through data preparation, augmentation, and model training
Suitable for beginners who want to get started with building Neural Networks in PyTorch
Led by an experienced instructor with a proven track record in the field
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Reviews summary
Low-quality fashion image classification course
According to students, very bad content delivery, mistakes in the projects, and errors in the code make this a poor quality course.
The projects and code contain mistakes.
"Mistakes in the projects - messy content"
"Errors in the code"
"The code does not work as the instructor types it."
The instructor makes a lot of mistakes and does not update the code or inform students.
"Very bad content delivery."
"Errors in the code: the code does not work as the instructor types it."
"The code is corrected later by the instructor, but he does not update the students (it's run on the bakground)."
Activities
Coming soon
We're preparing activities for
Fashion Image Classification using CNNs in Pytorch.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Fashion Image Classification using CNNs in Pytorch will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers specialize in CNNs, which are at the core of many computer vision tasks. For example, a Computer Vision Engineer at Apple might use CNNs to develop algorithms for object detection in photos. The Fashion Image Classification project is a good starting point for anyone interested in entering this field.
Software Engineer, Deep Learning
Software Engineer, Deep Learning
Software Engineers specializing in Deep Learning may use Convolutional Neural Networks for a wide variety of tasks, including image classification, object detection, and facial recognition. For example, a Deep Learning Software Engineer at Nvidia might use CNNs to help build autonomous driving systems. The Fashion Image Classification project will provide students with many transferable skills for success in this field.
Data Scientist
Data Scientist
Data Scientists may use Convolutional Neural Networks to process large datasets comprised of images. For example, a Data Scientist working for Google might use CNNs as part of an algorithm for training a self-driving car to classify surrounding vehicles in real-time. The Fashion Image Classification project teaches students the basics of CNNs, making it a good starting point for anyone who wants to enter this field.
Research Scientist, Artificial Intelligence
Research Scientist, Artificial Intelligence
Research Scientists in Artificial Intelligence may use Convolutional Neural Networks as part of their research on developing novel AI algorithms. For example, a Research Scientist at OpenAI might use CNNs to build new models for generating images. This project can help students learn about CNNs and build a foundation for this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers may use Convolutional Neural Networks for many purposes, such as training predictive models or creating image recognition algorithms. For example, a Machine Learning Engineer at Facebook may use CNNs to develop an algorithm for optimizing user engagement in photos. The Fashion Image Classification project can help students gain some of the skills needed to succeed in this field.
Data Engineer
Data Engineer
Data Engineers may use Convolutional Neural Networks for data exploration and feature engineering, although this is a less common application. For example, a Data Engineer working for a healthcare company might use CNNs to extract features from medical images. The Fashion Image Classification project will introduce students to CNNs and help them build a foundation for success in this field.
Data Analyst
Data Analyst
Data Analysts may use Convolutional Neural Networks to analyze large datasets comprised of images, although this is a less common application. For example, a Data Analyst working for an e-commerce company might use CNNs to understand user behavior on their site. The Fashion Image Classification project will introduce students to CNNs and help them build a foundation for success in this field.
Product Manager
Product Manager
Product Managers in the tech industry may need to understand Convolutional Neural Networks in order to make informed decisions about product development. For example, a Product Manager at Google might need to understand how CNNs work in order to assess the feasibility of a new image recognition feature for Google Photos. This project will introduce students to CNNs and help them build a foundation for success in this field.
Cloud Architect
Cloud Architect
Cloud Architects may need to understand Convolutional Neural Networks in order to design and implement cloud-based solutions for their clients. For example, a Cloud Architect at Amazon Web Services might need to know how to deploy CNNs on AWS. This project will introduce students to CNNs and help them build a foundation for success in this field.
Business Analyst
Business Analyst
Business Analysts may use Convolutional Neural Networks to analyze data and identify trends for their clients, although this is a less common application. For example, a Business Analyst working for a marketing firm might use CNNs to analyze images of social media posts in order to understand how customers are responding to a new advertising campaign. This project will introduce students to CNNs and help them build a foundation for success in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts may use Convolutional Neural Networks to analyze financial data and make investment decisions, although this is a less common application. For example, a Quantitative Analyst working for a hedge fund might use CNNs to analyze images of stock charts in order to identify trading opportunities. This project will introduce students to CNNs and help them build a foundation for success in this field.
Technical Writer
Technical Writer
Technical Writers may need to understand Convolutional Neural Networks in order to write documentation or training materials for CNN-based technologies. For example, a Technical Writer working for a software company might need to understand how CNNs work in order to write a user manual for a new image recognition software. This project will introduce students to CNNs and help them build a foundation for success in this field.
IT Project Manager
IT Project Manager
IT Project Managers may need to understand Convolutional Neural Networks in order to manage projects that are developing or using CNN-based technologies. For example, an IT Project Manager working for a government agency might need to understand how CNNs work in order to manage a project that is developing a new facial recognition system for law enforcement. This project will introduce students to CNNs and help them build a foundation for success in this field.
Sales Engineer
Sales Engineer
Sales Engineers may need to understand Convolutional Neural Networks in order to sell and support CNN-based technologies to their customers. For example, a Sales Engineer working for a hardware company might need to understand how CNNs work in order to sell a new GPU that is designed for running CNNs. This project will introduce students to CNNs and help them build a foundation for success in this field.
Software Engineering Manager
Software Engineering Manager
Software Engineering Managers may need to understand Convolutional Neural Networks in order to lead and manage teams of engineers who are working on CNN-based projects. For example, a Software Engineering Manager at Microsoft might need to understand how CNNs work in order to make decisions about resource allocation on a project that is developing a new image recognition system for Windows. This project will introduce students to CNNs and help them build a foundation for success in this field.
For more career information including salaries, visit:
OpenCourser.com/course/3sxszl/fashion
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Fashion Image Classification using CNNs in Pytorch.
Save
Comprehensive overview of convolutional neural networks (CNNs). It covers the basics of CNNs, as well as more advanced topics such as residual networks, inception networks, and generative adversarial networks. The book also includes a number of exercises and projects that allow readers to practice what they have learned.
Save
Comprehensive overview of computer vision. It covers the basics of computer vision, as well as more advanced topics such as image segmentation, object detection, and tracking. The book also includes a number of exercises and projects that allow readers to practice what they have learned.
Provides a comprehensive overview of PyTorch, a popular deep learning framework. It covers the basics of neural networks, convolutional neural networks, and recurrent neural networks. The book also includes a number of case studies that demonstrate how PyTorch can be used to solve real-world problems.
Guide to Keras, a high-level deep learning library for Python. It covers the basics of Keras, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. The book also includes a number of exercises and projects that allow readers to practice what they have learned.
Guide to machine learning with R. It covers the basics of machine learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. The book also includes a number of exercises and projects that allow readers to practice what they have learned.
Practical guide to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers the basics of machine learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. The book also includes a number of exercises and projects that allow readers to practice what they have learned.
Visual guide to deep learning. It uses clear and concise language to explain the concepts of deep learning, and it includes a number of helpful illustrations. This book good resource for learners who want to get a visual overview of deep learning.
Practical guide to deep learning for computer vision. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. The book also includes a number of exercises and projects that allow readers to practice what they have learned.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3sxszl/fashion
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Mohammed Murtuza Qureshi
Language
English
Good to know
Teaches a deep learning framework that is widely used in industry and academia
Provides a practical understanding of creating Neural Networks in PyTorch
Guides learners through data preparation, augmentation, and model training
Suitable for beginners who want to get started with building Neural Networks in PyTorch
Led by an experienced instructor with a proven track record in the field
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