Convolutional Neural Networks in TensorFlow from Coursera

What's inside

Syllabus

Exploring a Larger Dataset

In the first course in this specialization, you had an introduction to TensorFlow, and how, with its high level APIs you could do basic image classification, and you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification!In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification!

Augmentation: A technique to avoid overfitting

You've heard the term overfitting a number of times to this point. Overfitting is simply the concept of being over specialized in training -- namely that your model is very good at classifying what it is trained for, but not so good at classifying things that it hasn't seen. In order to generalize your model more effectively, you will of course need a greater breadth of samples to train it on. That's not always possible, but a nice potential shortcut to this is Image Augmentation, where you tweak the training set to potentially increase the diversity of subjects it covers. You'll learn all about that this week!

Transfer Learning

Building models for yourself is great, and can be very powerful. But, as you've seen, you can be limited by the data you have on hand. Not everybody has access to massive datasets or the compute power that's needed to train them effectively. Transfer learning can help solve this -- where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario. This is Transfer learning, and you'll look into that this week!

Multiclass Classifications

You've come a long way, Congratulations! One more thing to do before we move off of ConvNets to the next module, and that's to go beyond binary classification. Each of the examples you've done so far involved classifying one thing or another -- horse or human, cat or dog. When moving beyond binary into Categorical classification there are some coding considerations you need to take into account. You'll look at them this week!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds upon the foundational principles of Machine Learning and Deep Learning, expanding into practical applications and hands-on implementation

Taught by Laurence Moroney, a recognized expert in TensorFlow and AI development

Provides hands-on labs and interactive materials, enabling learners to apply concepts in a practical setting

Suitable for software developers who seek to develop scalable, AI-powered algorithms using TensorFlow

Covers advanced techniques for improving computer vision models, including image augmentation and transfer learning

Requires familiarity with basic TensorFlow and image classification concepts, recommending prerequisite courses for stronger foundational knowledge

Reviews summary

Practical cnn in tf

learners say this course provides a "step by step" introduction to Convolutional Neural Networks (CNNs) using the TensorFlow library. It is a practical, hands-on course that focuses on the implementation of CNNs. The course is well-structured and easy to follow, with short video lectures and coding assignments. The coding assignments are designed to reinforce the concepts learned in the lectures and to provide students with experience in building and training CNN models. The course covers a wide range of topics, including: * The basics of CNNs * Different types of CNN architectures * Training and evaluating CNNs * Using CNNs for image classification and object detection This course is a good choice for anyone who wants to learn how to use CNNs for image classification and object detection. It is also a good choice for anyone who wants to understand the underlying principles of CNNs.

The course features include: * Short video lectures and coding assignments * Step-by-step introduction to CNNs * Implementation of CNNs * Focus on image classification and object detection

"learners say this course provides a "step by step" introduction to Convolutional Neural Networks (CNNs) using the TensorFlow library."

"The course is well-structured and easy to follow, with short video lectures and coding assignments."

"The coding assignments are designed to reinforce the concepts learned in the lectures and to provide students with experience in building and training CNN models."

"The course covers a wide range of topics, including: * The basics of CNNs * Different types of CNN architectures * Training and evaluating CNNs * Using CNNs for image classification and object detection"

The course is well-structured and organized, making it easy to follow and learn from.

"learners say this course provides a "step by step" introduction to Convolutional Neural Networks (CNNs) using the TensorFlow library."

"The course is well-structured and easy to follow, with short video lectures and coding assignments."

The course is practical and hands-on, focusing on the implementation of CNNs. This provides students with experience in building and training CNN models.

"learners say this course provides a "step by step" introduction to Convolutional Neural Networks (CNNs) using the TensorFlow library."

"It is a practical, hands-on course that focuses on the implementation of CNNs."

One potential challenge with this course is that it primarily focuses on the implementation of CNNs, rather than providing a comprehensive overview of the theoretical foundations of CNNs. For learners who are new to CNNs, it may be helpful to supplement this course with additional resources that provide a more in-depth treatment of the theoretical aspects of CNNs. Additionally, some of the coding assignments may require learners to have some prior experience with Python and TensorFlow, so it may be helpful for learners to review these topics before starting the course.

Career center

Learners who complete Convolutional Neural Networks in TensorFlow will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you build the skills you need to become a Machine Learning Engineer, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Machine Learning Engineer, you would be responsible for designing, developing, and deploying machine learning models to solve real-world problems. If you are interested in a career in machine learning, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists analyze data to identify trends and patterns, and develop models to predict future outcomes. This course can help you build the skills you need to become a Data Scientist, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Data Scientist, you would be responsible for collecting, cleaning, and analyzing data to identify trends and patterns. You would also develop models to predict future outcomes and make recommendations. If you are interested in a career in data science, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and maintain software systems. This course can help you build the skills you need to become a Software Engineer, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Software Engineer, you would be responsible for designing, developing, and maintaining software systems. If you are interested in a career in software engineering, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. This course can help you build the skills you need to become a Data Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Data Analyst, you would be responsible for collecting, cleaning, and analyzing data to identify trends and patterns. If you are interested in a career in data analysis, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. This course can help you build the skills you need to become a Financial Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Financial Analyst, you would be responsible for analyzing financial data to make investment recommendations. If you are interested in a career in financial analysis, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Financial Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. This course can help you build the skills you need to become a Product Manager, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Product Manager, you would be responsible for the development and launch of new products. If you are interested in a career in product management, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Product Manager

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help you build the skills you need to become a Quantitative Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Quantitative Analyst, you would be responsible for using mathematical and statistical models to analyze financial data. If you are interested in a career in quantitative analysis, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market Researchers collect and analyze data about consumers and markets. This course can help you build the skills you need to become a Market Researcher, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Market Researcher, you would be responsible for collecting and analyzing data about consumers and markets. If you are interested in a career in market research, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Market Researcher

User Experience Researcher

User Experience Researchers study how users interact with products and services. This course can help you build the skills you need to become a User Experience Researcher, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a User Experience Researcher, you would be responsible for studying how users interact with products and services. If you are interested in a career in user experience research, this course can help you develop the skills you need to succeed.

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Management Consultant

Management Consultants help organizations improve their performance. This course can help you build the skills you need to become a Management Consultant, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Management Consultant, you would be responsible for helping organizations improve their performance. If you are interested in a career in management consulting, this course can help you develop the skills you need to succeed.

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Data Visualization Engineer

Data Visualization Engineers design and develop data visualizations to communicate data insights. This course can help you build the skills you need to become a Data Visualization Engineer, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Data Visualization Engineer, you would be responsible for designing and developing data visualizations to communicate data insights. If you are interested in a career in data visualization, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Data Visualization Engineer

Business Analyst

Business Analysts use data to identify opportunities and solve problems. This course can help you build the skills you need to become a Business Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Business Analyst, you would be responsible for using data to identify opportunities and solve problems. If you are interested in a career in business analysis, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. This course can help you build the skills you need to become an Operations Research Analyst, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As an Operations Research Analyst, you would be responsible for using mathematical and statistical models to solve business problems. If you are interested in a career in operations research, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians collect, analyze, and interpret data. This course can help you build the skills you need to become a Statistician, including machine learning, data analysis, and statistical modeling. You will also learn how to use TensorFlow, a popular open-source framework for machine learning. As a Statistician, you would be responsible for collecting, analyzing, and interpreting data. If you are interested in a career in statistics, this course can help you develop the skills you need to succeed.

See salaries and explore the career path for Statistician

Reading list

We've selected seven 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 Convolutional Neural Networks in TensorFlow.

Deep Learning with Python, Second Edition

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Concurrent NumPy in Python

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Deep Learning

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Provides a comprehensive overview of generative adversarial networks, including their theoretical foundations, their practical applications, and their ethical implications. It is particularly useful for those who are new to generative adversarial networks or who want to learn more about their foundations.

Deep Learning (Adaptive Computation and Machine...

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Deep Learning for Autonomous Vehicle Control

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Provides a comprehensive overview of deep reinforcement learning, including its theoretical foundations, its practical applications, and its ethical implications. It is particularly useful for those who are new to deep reinforcement learning or who want to learn more about its foundations.

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Cross-Lingual Word Embeddings

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Provides a comprehensive overview of natural language processing with deep learning, including its theoretical foundations, its practical applications, and its ethical implications. It is particularly useful for those who are new to natural language processing with deep learning or who want to learn more about its foundations.

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Interpretable Machine Learning

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Provides a comprehensive overview of interpretable machine learning, including its theoretical foundations, its practical applications, and its ethical implications. It is particularly useful for those who are new to interpretable machine learning or who want to learn more about its foundations.

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Convolutional Neural Networks in TensorFlow

What's inside

Syllabus

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Reviews summary

Practical cnn in tf

Career center

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