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Deep Learning - Convolutional Neural Networks

This course is a part of the Deep Learning Foundations Nanodegree Program.

Convolutional networks have revolutionized the field of computer vision. Hand coded feature detection has been replaced with these networks that learn features from the data itself. In this course, you'll learn how to build convolutional networks with TensorFlow, then use them for image classification.

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Teaches methods for building convolutional networks, which are highly relevant to computer vision, image recognition, and image classification

Develops skills in using TensorFlow, a popular deep learning framework

Offered by Udacity, a provider known for its high-quality online courses and programs

Taught by Mat Leonard, an expert in computer vision and artificial intelligence

Part of the Deep Learning Foundations Nanodegree Program, providing a structured learning path

Requires Python and TensorFlow knowledge

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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 Deep Learning - Convolutional Neural Networks with these activities:

Review Python programming concepts

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Ensure a strong foundation in Python programming, which is necessary for working with TensorFlow.

Browse courses on Python

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Review basic Python syntax and data structures
Practice writing Python functions and classes

Read Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

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Provide a comprehensive overview of deep learning, including convolutional networks.

View Deep Learning on Amazon

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Read the chapters on convolutional neural networks
Work through the exercises in the book

Practice using Python for data manipulation and analysis

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Demonstrate proficiency in using Python for data manipulation and analysis, which is essential for working with image datasets.

Browse courses on Python

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Practice loading and cleaning image data using Python
Practice visualizing data using Python
Practice performing data analysis using Python

Four other activities

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Follow tutorials on building convolutional networks with TensorFlow

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Provide additional guidance and examples on building convolutional networks with TensorFlow.

Browse courses on TensorFlow

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Find tutorials on building convolutional networks with TensorFlow
Follow a tutorial to build a convolutional network for image classification

Practice using TensorFlow to build neural networks

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Reinforce understanding of how TensorFlow is used to build and train neural networks, specifically convolutional networks.

Browse courses on TensorFlow

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Practice building a convolutional network for image classification
Practice training a convolutional network on a dataset of images
Practice evaluating the performance of a convolutional network

Write a blog post on how to build a convolutional network with TensorFlow

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Demonstrate understanding of convolutional networks and TensorFlow by writing about it.

Browse courses on TensorFlow

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Outline the key concepts of convolutional networks
Explain how to build a convolutional network with TensorFlow
Provide examples of how to use a convolutional network for image classification

Build a convolutional network for a specific image classification task

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Provide a practical application of convolutional networks for image classification.

Browse courses on TensorFlow

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Define the image classification task
Collect and prepare the image dataset
Build a convolutional network architecture
Train the convolutional network
Evaluate the performance of the convolutional network

Career center

Learners who complete Deep Learning - Convolutional Neural Networks will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

Convolutional neural networks are at the heart of computer vision. This course provides engineers with the knowledge and skills necessary to develop and implement computer vision solutions in a variety of industries, from retail to healthcare. The course's focus on TensorFlow allows engineers to quickly build and deploy models that can solve real-world problems.

See salaries and explore the career path for Computer Vision Engineer

Artificial Intelligence Engineer

Convolutional neural networks are a key technology in the field of artificial intelligence. This course will help artificial intelligence engineers build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement AI solutions to a wide range of problems. The course will also help engineers understand the ethical implications of AI and how to develop AI systems that are fair and responsible.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

Machine Learning engineers use convolutional neural networks to achieve state of the art results in computer vision. This course can help machine learning engineers build a strong foundation with one of the most important tools used in their field. The course's focus on TensorFlow will allow engineers to jump right into a production environment and start implementing solutions to a wide range of problems.

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Research Scientist

Convolutional neural networks are a powerful tool for research scientists in a variety of fields, including computer vision, natural language processing, and speech recognition. This course will help researchers build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement state-of-the-art solutions to research problems.

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Data Analyst

Convolutional neural networks are increasingly used by data analysts to gain insights from image data. This course will help analysts build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement solutions to a wide range of problems. The course will also help analysts understand how to interpret and communicate the results of their analyses.

See salaries and explore the career path for Data Analyst

Healthcare Data Analyst

Convolutional neural networks are increasingly used in healthcare to analyze medical images and data. This course will help healthcare data analysts build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement solutions to a wide range of healthcare problems. The course will also help analysts understand the challenges of developing and deploying healthcare solutions in the real world.

See salaries and explore the career path for Healthcare Data Analyst

Data Scientist

Data scientists today use convolutional neural networks to gain useful insights from massive datasets. Due to the amount of unstructured image data in today's digital world, this course can help data scientists excell in organizing and presenting this often mind boggling data in such a way that the insights can contribute to informed business decisions. The mathematical nature of convolutional networks should be familiar to most data scientists, but this course is specifically designed to help data scientists gain comfort with the Python and TensorFlow libraries.

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Game Developer

Convolutional neural networks are used in a variety of game development applications, such as character recognition, object detection, and scene generation. This course will help game developers build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement cutting-edge games. The course will also help developers understand the challenges of developing and deploying games in the real world.

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Computer Graphics Engineer

Convolutional neural networks are increasingly used in computer graphics to create realistic images and animations. This course will help computer graphics engineers build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement cutting-edge graphics solutions. The course will also help engineers understand the challenges of developing and deploying computer graphics systems in the real world.

See salaries and explore the career path for Computer Graphics Engineer

Business Analyst

Convolutional neural networks are increasingly used in business to analyze data and make predictions. This course will help business analysts build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement solutions to a wide range of business problems. The course will also help analysts understand the challenges of developing and deploying business solutions in the real world.

See salaries and explore the career path for Business Analyst

Financial Analyst

Convolutional neural networks are used in a variety of financial applications, such as fraud detection, risk assessment, and portfolio management. This course will help financial analysts build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement cutting-edge financial solutions. The course will also help analysts understand the challenges of developing and deploying financial solutions in the real world.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Convolutional neural networks are used in a variety of marketing applications, such as image recognition, object detection, and scene generation. This course will help marketing analysts build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement cutting-edge marketing solutions. The course will also help analysts understand the challenges of developing and deploying marketing solutions in the real world.

See salaries and explore the career path for Marketing Analyst

Quantitative Analyst

Convolutional neural networks provide breakthroughs in many different fields, including finance. Quantitative analysts can use this course to learn how to apply deep learning techniques to the analysis of financial data to gain insights that improve decision making. The course's practical approach to TensorFlow will allow quants to build and deploy models quickly and easily.

See salaries and explore the career path for Quantitative Analyst

Robotics Engineer

Convolutional neural networks are used in a variety of robotics applications, such as object recognition, navigation, and manipulation. This course will help robotics engineers build a strong foundation in convolutional neural networks and TensorFlow, enabling them to develop and implement robotic systems that can solve real-world problems. The course will also help engineers understand the challenges of developing and deploying robotic systems in the real world.

See salaries and explore the career path for Robotics Engineer

Software Engineer

Convolutional neural networks are the technology behind many popular software products. This course can help you master the technical skills necessary to develop and implement these in demand software solutions. Whether you're looking to add features to existing products or develop your own, this course will help you build a strong foundation using TensorFlow, a top library used in industry.

See salaries and explore the career path for Software Engineer

Reading list

We've selected 16 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 Deep Learning - Convolutional Neural Networks.

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Provides a comprehensive overview of deep learning, including convolutional neural networks. It valuable resource for anyone who wants to learn more about this field.

Deep Learning - Convolutional Neural Networks

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