We may earn an affiliate commission when you visit our partners.
Ari Anastassiou
In this 2 hour-long project, you will learn how to approach an image classification task using TensorFlow. You will learn how to effectively preprocess your data to improve model generalizability, as well as build a performant modeling pipeline. Furthermore, you will learn how to accurately evaluate model performance using a confusion matrix; how to interpret results; and how to ask poignant questions about your dataset. Finally, you will fine-tune an existing, state-of-the-art-ready model to improve performance further.
Read more
In this 2 hour-long project, you will learn how to approach an image classification task using TensorFlow. You will learn how to effectively preprocess your data to improve model generalizability, as well as build a performant modeling pipeline. Furthermore, you will learn how to accurately evaluate model performance using a confusion matrix; how to interpret results; and how to ask poignant questions about your dataset. Finally, you will fine-tune an existing, state-of-the-art-ready model to improve performance further.
Read more
In this 2 hour-long project, you will learn how to approach an image classification task using TensorFlow. You will learn how to effectively preprocess your data to improve model generalizability, as well as build a performant modeling pipeline. Furthermore, you will learn how to accurately evaluate model performance using a confusion matrix; how to interpret results; and how to ask poignant questions about your dataset. Finally, you will fine-tune an existing, state-of-the-art-ready model to improve performance further.
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/jnx0e0/fine
What's inside
Syllabus
Fine-tuning Convolutional Networks to Classify Dog Breeds
In this 2 hour-long project, you will learn how to approach an image classification task using TensorFlow. You will learn how to effectively preprocess your data to improve model generalizability, as well as build a performant modeling pipeline. Furthermore, you will learn how to accurately evaluate model performance using a confusion matrix; how to interpret results; and how to ask poignant questions about your dataset. Finally, you will fine-tune an existing, state-of-the-art-ready model to improve performance further.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores how to classify dog breeds using TensorFlow, a valuable skill in machine learning
Provides hands-on experience with image classification, enhancing practical skills
Emphasizes data preprocessing, model building, evaluation, and fine-tuning, covering key aspects of machine learning workflow
Focuses on real-world application, appealing to learners seeking practical knowledge
Utilizes a pre-trained model, reducing the need for extensive data collection and training time
Suitable for learners with prior knowledge in machine learning, particularly those interested in image classification
Save this course
Save Fine-tuning Convolutional Networks to Classify Dog Breeds to your list so you can find it easily later:
Save
Save
Reviews summary
Challenging dog breed classification course
Learners say this course is challenging with mixed reviews. Some students appreciate the in-depth explanation of how to preprocess data for a convolutional neural network (CNN), build a CNN, or use transfer learning to create an image classification model. At the same time, other students say that the code does not work and they have to search for answers outside the course.
Students find the explanation on how to preprocess data for a CNN to be in-depth.
"An extended example of how to preprocess data for a CNN"
Students find the course material to be difficult and challenging.
"The Course was not what I expected"
"UNPLEASENT EXPERIENCE!!!!!"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Fine-tuning Convolutional Networks to Classify Dog Breeds with these
activities:
Connect with Experienced Data Scientists
Show steps
Provides opportunities to learn from experts in the field.
Show steps
-
Attend industry events or online forums.
-
Reach out to data scientists on LinkedIn.
Organize Course Notes and Materials
Show steps
Ensures a well-organized and accessible study environment.
Show steps
-
Create a dedicated folder for course materials.
-
File lecture notes, assignments, and quizzes.
Review Machine Learning Concepts
Show steps
Refreshes foundational knowledge in machine learning.
Browse courses on
Machine Learning
Show steps
-
Review supervised and unsupervised learning algorithms.
-
Go over common machine learning metrics.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Image Preprocessing Techniques
Show steps
Reinforces understanding of image preprocessing techniques.
Browse courses on
Image Preprocessing
Show steps
-
Perform data augmentation on a dataset of images.
-
Compare the effects of different preprocessing techniques on model performance.
Build a Simple Image Classifier
Show steps
Provides hands-on experience in building and evaluating an image classification model.
Browse courses on
Convolutional Neural Networks
Show steps
-
Gather and preprocess a dataset of images.
-
Use TensorFlow to build a CNN model.
-
Train and evaluate the model.
Explore Advanced TensorFlow Techniques
Show steps
Expands knowledge of TensorFlow and deep learning techniques not covered in the course.
Browse courses on
TensorFlow
Show steps
-
Follow a tutorial on using advanced TensorFlow features.
-
Apply the new techniques to a project or task.
Contribute to a TensorFlow Project
Show steps
Provides practical experience in contributing to the open-source community.
Browse courses on
Open Source
Show steps
-
Identify a bug or feature request in a TensorFlow project.
-
Create a pull request to fix the issue or implement the feature.
Connect with Experienced Data Scientists
Show steps
Provides opportunities to learn from experts in the field.
Show steps
Show steps
Show steps
- Attend industry events or online forums.
- Reach out to data scientists on LinkedIn.
Organize Course Notes and Materials
Show steps
Ensures a well-organized and accessible study environment.
Show steps
Show steps
Show steps
- Create a dedicated folder for course materials.
- File lecture notes, assignments, and quizzes.
Review Machine Learning Concepts
Show steps
Refreshes foundational knowledge in machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review supervised and unsupervised learning algorithms.
- Go over common machine learning metrics.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Image Preprocessing Techniques
Show steps
Reinforces understanding of image preprocessing techniques.
Browse courses on
Image Preprocessing
Show steps
Show steps
Show steps
- Perform data augmentation on a dataset of images.
- Compare the effects of different preprocessing techniques on model performance.
Build a Simple Image Classifier
Show steps
Provides hands-on experience in building and evaluating an image classification model.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Gather and preprocess a dataset of images.
- Use TensorFlow to build a CNN model.
- Train and evaluate the model.
Explore Advanced TensorFlow Techniques
Show steps
Expands knowledge of TensorFlow and deep learning techniques not covered in the course.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow a tutorial on using advanced TensorFlow features.
- Apply the new techniques to a project or task.
Contribute to a TensorFlow Project
Show steps
Provides practical experience in contributing to the open-source community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify a bug or feature request in a TensorFlow project.
- Create a pull request to fix the issue or implement the feature.
Career center
Learners who complete Fine-tuning Convolutional Networks to Classify Dog Breeds will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems. This course provides the foundational knowledge required for this career by introducing convolutional networks and fine-tuning techniques. Especially valuable is the hands-on experience with building a model to classify dog breeds, as this showcases practical applications of these techniques.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers build and maintain AI systems. This course provides a strong foundation for this career by introducing convolutional networks, fine-tuning techniques, and TensorFlow. Additionally, the emphasis on evaluating model performance and asking poignant questions about datasets is crucial for the development and improvement of AI systems.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers build and maintain deep learning models. This course provides foundational knowledge in fine-tuning convolutional networks and using TensorFlow, a popular deep learning library. Furthermore, the hands-on experience in building a model to classify dog breeds is particularly valuable for those seeking a career in deep learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. Gaining skills in TensorFlow and model training through this project can help prepare one for roles like this. Moreover, the course's focus on evaluating model performance using confusion matrices and interpreting results is essential in this field for monitoring and improving model quality.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. This course can provide foundational knowledge of these skills in order to help one prepare for a role as a data scientist. Especially useful is the emphasis on preprocessing data to improve model generalizability and building a performant modeling pipeline.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. This course can provide foundational knowledge in data preprocessing, model building, and model evaluation, which are essential skills for data analysts. Additionally, the hands-on experience with TensorFlow and building a model to classify dog breeds can be valuable for those seeking a career in data analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides foundational knowledge in model building and evaluation, which are essential skills for quantitative analysts. Additionally, the hands-on experience with TensorFlow and building a model to classify dog breeds can be valuable for those seeking a career in quantitative analysis.
Software Engineer
Software Engineer
Software Engineers build and maintain software systems. While this course may only partially prepare one for this career path, it is impactful because it provides experience with TensorFlow, a popular deep learning library, as well as preprocessing data and building modeling pipelines.
Research Scientist
Research Scientist
Research Scientists conduct research in various scientific fields. This course may be useful for those seeking a career in research, as it provides foundational knowledge in model building and evaluation.
Business Analyst
Business Analyst
Business Analysts use data to solve business problems. This course may be useful for those seeking a career in business analysis, as it provides foundational knowledge in data preprocessing, model building, and model evaluation, which are essential skills for business analysts.
Product Manager
Product Manager
Product Managers develop and manage products. This course may be useful for those seeking a career in product management, as it provides foundational knowledge in model building and evaluation, which can be valuable in understanding and developing data-driven products.
Consultant
Consultant
Consultants provide advice and guidance to businesses. This course may be useful for those seeking a career in consulting, as it provides foundational knowledge in model building and evaluation, which can be valuable in providing data-driven insights to clients.
Statistician
Statistician
Statisticians use statistical methods to analyze data. This course may be useful for those seeking a career in statistics, as it provides foundational knowledge in model building and evaluation, which are essential skills for statisticians.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. This course may be useful for those seeking a career in actuarial science, as it provides foundational knowledge in model building and evaluation, which are essential skills for actuaries.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course may be useful for those seeking a career in financial analysis, as it provides foundational knowledge in model building and evaluation, which can be valuable in analyzing financial data and making investment decisions.
For more career information including salaries, visit:
OpenCourser.com/course/jnx0e0/fine
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
Fine-tuning Convolutional Networks to Classify Dog Breeds.
Save
A seminal work providing a comprehensive overview of convolutional neural networks, covering mathematical foundations, architectures, and applications.
This comprehensive guide provides a solid foundation in deep learning concepts and techniques and practical implementation guidance using popular Python libraries.
Save
A comprehensive textbook on computer vision, covering foundational theories, algorithms, and applications in various fields such as image processing, object recognition, and image understanding.
A comprehensive reference on pattern recognition and machine learning, covering statistical models, Bayesian inference, and neural networks for image classification and other computer vision tasks.
A practical guide to building and deploying machine learning models using popular open-source Python libraries, covering foundational topics and real-world applications.
Save
A practical guide to advanced deep learning techniques and architectures, with a focus on implementing complex models using TensorFlow and Keras.
A collection of lecture notes on deep learning architectures and their applications in artificial intelligence, providing insights into the design and training of deep neural networks.
A comprehensive resource on deep learning for computer vision tasks, with a focus on using the Keras and TensorFlow libraries for model development and deployment.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jnx0e0/fine
Share
Similar courses
Here are nine courses similar to
Fine-tuning Convolutional Networks to Classify Dog Breeds.
Optimizing Neural Networks for Efficient Data Processing
OpenCourser.com/course/e00hjz/optimizing
Optimizing Neural Networks for Efficient Data Processing
Optimize Model Performance with Power BI
OpenCourser.com/course/c2c0u7/optimize
Optimize Model Performance with Power BI
Prompt Engineering Best Practices
OpenCourser.com/course/6ffqnc/prompt
Prompt Engineering Best Practices
Emotion AI: Facial Key-points Detection
OpenCourser.com/course/8fzsxx/emotion
Emotion AI: Facial Key-points Detection
Managing SSAS Models
OpenCourser.com/course/uxsbxg/managing
Managing SSAS Models
Evaluating Model Effectiveness in Microsoft Azure
OpenCourser.com/course/9jp885/evaluating
Evaluating Model Effectiveness in Microsoft Azure
Facial Expression Classification Using Residual Neural Nets
OpenCourser.com/course/spohme/facial
Facial Expression Classification Using Residual Neural...
Building Features from Nominal and Numeric Data in Microsoft Azure
OpenCourser.com/course/xuupl5/building
Building Features from Nominal and Numeric Data in...
Deep Learning with Caffe
OpenCourser.com/course/thoh9g/deep
Deep Learning with Caffe
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ari Anastassiou
Language
English
Good to know
Explores how to classify dog breeds using TensorFlow, a valuable skill in machine learning
Provides hands-on experience with image classification, enhancing practical skills
Emphasizes data preprocessing, model building, evaluation, and fine-tuning, covering key aspects of machine learning workflow
Focuses on real-world application, appealing to learners seeking practical knowledge
Utilizes a pre-trained model, reducing the need for extensive data collection and training time
Suitable for learners with prior knowledge in machine learning, particularly those interested in image classification
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser