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Josh Bernhard ,
Michael Virgo,
Mat Leonard,
Andrew Paster,
Jennifer Staab,
Dan Romuald Mbanga,
Cezanne Camacho,
Sean Carrell,
Jay Alammar,
Luis Serrano,
and
Juan Delgado
Discover the power of neural networks with our online comprehensive TensorFlow course. Get an Introduction to Neural Networks and their applications. Enroll now
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Multivariable calculus
- Basic descriptive statistics
- Python for data science
- Basic probability
- Linear algebra
Read more
Discover the power of neural networks with our online comprehensive TensorFlow course. Get an Introduction to Neural Networks and their applications. Enroll now
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Multivariable calculus
- Basic descriptive statistics
- Python for data science
- Basic probability
- Linear algebra
Read more
Discover the power of neural networks with our online comprehensive TensorFlow course. Get an Introduction to Neural Networks and their applications. Enroll now
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Multivariable calculus
- Basic descriptive statistics
- Python for data science
- Basic probability
- Linear algebra
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/v7118t/introduction
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What's inside
Syllabus
Meet your instructors, get a short overview of what you'll be learning, check your prerequisites, and learn how to use the workspaces and notebooks found throughout the lessons.
Read more
Syllabus
Meet your instructors, get a short overview of what you'll be learning, check your prerequisites, and learn how to use the workspaces and notebooks found throughout the lessons.
Read more
In this lesson, Luis will give you solid foundations on deep learning and neural networks. You'll also implement gradient descent and backpropagation in Python right here in the classroom.
Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.
Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
Learn how to use TensorFlow for building deep learning models.
In this project, you'll build a Python application that can train an image classifier on a dataset, then predict new images using the trained model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by a team with substantial experience in industry, research, and education
Introduces the latest methods and techniques in neural networks and deep learning through interactive materials and hands-on labs
Focuses on the fundamentals of neural networks and their applications, suitable for beginners and those looking to strengthen their foundational skills
Covers advanced topics and real-world techniques in deep learning, making it ideal for intermediate learners seeking to advance their expertise
Teaches TensorFlow, a widely used industry-standard framework for deep learning
Emphasizes the practical aspects of implementing neural networks through a capstone project involving building a custom image classifier
Save this course
Save Introduction to Neural Networks with TensorFlow to your list so you can find it easily later:
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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 Introduction to Neural Networks with TensorFlow with these
activities:
Organize Course Materials
Show steps
Establish a strong foundation for your learning by organizing and reviewing your course materials, including lecture notes, assignments, and quizzes.
Show steps
-
Create a dedicated folder or notebook for all course materials.
-
Categorize and file materials based on topic or week.
-
Review materials regularly to reinforce your understanding.
Refresh Your Python Skills
Show steps
Ensure your Python skills are up to par by reviewing basic concepts, refreshing your memory on data structures, and practicing problem-solving.
Browse courses on
Programming Fundamentals
Show steps
-
Review online tutorials or documentation on Python basics.
-
Solve coding exercises on platforms like LeetCode or HackerRank.
Review Introduction to the Deep Learning with Python: A Practical Guide
Show steps
Build a solid foundation in deep learning principles and techniques by reviewing this comprehensive and easy-to-follow guide.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
-
Read through the book's introduction and first few chapters to grasp the fundamentals of deep learning.
-
Focus on understanding the concepts of neural networks, layers, and activation functions.
-
Work through the exercises and examples provided in the book to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow TensorFlow Tutorials for Beginners
Show steps
Enhance your practical skills by working through hands-on tutorials that guide you through building and training TensorFlow models.
Browse courses on
TensorFlow
Show steps
-
Visit the TensorFlow website and explore the tutorials section.
-
Choose a beginner-friendly tutorial and follow the instructions step-by-step.
-
Experiment with different hyperparameters and observe how they impact model performance.
Join a TensorFlow Study Group
Show steps
Enhance your learning experience by collaborating with peers in a study group, exchanging knowledge, and tackling challenges collectively.
Browse courses on
TensorFlow
Show steps
-
Reach out to classmates or fellow TensorFlow enthusiasts to form a study group.
-
Establish a regular meeting schedule and agenda.
-
Take turns presenting concepts, discussing assignments, and solving problems together.
Solve LeetCode Problems on Neural Networks
Show steps
Challenge yourself and deepen your understanding of neural networks by solving LeetCode problems specifically designed for this topic.
Browse courses on
Neural Networks
Show steps
-
Create a LeetCode account and familiarize yourself with the platform.
-
Filter problems by topic using the tags section.
-
Start with easier problems and gradually progress to more challenging ones.
Contribute to TensorFlow Community
Show steps
Deepen your understanding of TensorFlow and contribute to the community by participating in open-source projects and discussions.
Browse courses on
TensorFlow
Show steps
-
Join the TensorFlow community on GitHub.
-
Identify a project or issue that aligns with your interests.
-
Submit a pull request or engage in discussions to share your knowledge and contribute to the project's development.
Organize Course Materials
Show steps
Establish a strong foundation for your learning by organizing and reviewing your course materials, including lecture notes, assignments, and quizzes.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for all course materials.
- Categorize and file materials based on topic or week.
- Review materials regularly to reinforce your understanding.
Refresh Your Python Skills
Show steps
Ensure your Python skills are up to par by reviewing basic concepts, refreshing your memory on data structures, and practicing problem-solving.
Browse courses on
Programming Fundamentals
Show steps
Show steps
Show steps
- Review online tutorials or documentation on Python basics.
- Solve coding exercises on platforms like LeetCode or HackerRank.
Review Introduction to the Deep Learning with Python: A Practical Guide
Show steps
Build a solid foundation in deep learning principles and techniques by reviewing this comprehensive and easy-to-follow guide.
View
Introduction to Machine Learning with Python: A...
on Amazon
Show steps
Show steps
Show steps
- Read through the book's introduction and first few chapters to grasp the fundamentals of deep learning.
- Focus on understanding the concepts of neural networks, layers, and activation functions.
- Work through the exercises and examples provided in the book to reinforce your understanding.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow TensorFlow Tutorials for Beginners
Show steps
Enhance your practical skills by working through hands-on tutorials that guide you through building and training TensorFlow models.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Visit the TensorFlow website and explore the tutorials section.
- Choose a beginner-friendly tutorial and follow the instructions step-by-step.
- Experiment with different hyperparameters and observe how they impact model performance.
Join a TensorFlow Study Group
Show steps
Enhance your learning experience by collaborating with peers in a study group, exchanging knowledge, and tackling challenges collectively.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Reach out to classmates or fellow TensorFlow enthusiasts to form a study group.
- Establish a regular meeting schedule and agenda.
- Take turns presenting concepts, discussing assignments, and solving problems together.
Solve LeetCode Problems on Neural Networks
Show steps
Challenge yourself and deepen your understanding of neural networks by solving LeetCode problems specifically designed for this topic.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Create a LeetCode account and familiarize yourself with the platform.
- Filter problems by topic using the tags section.
- Start with easier problems and gradually progress to more challenging ones.
Contribute to TensorFlow Community
Show steps
Deepen your understanding of TensorFlow and contribute to the community by participating in open-source projects and discussions.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Join the TensorFlow community on GitHub.
- Identify a project or issue that aligns with your interests.
- Submit a pull request or engage in discussions to share your knowledge and contribute to the project's development.
Career center
Learners who complete Introduction to Neural Networks with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models that can learn from data. They work with Data Scientists to determine the best algorithms for a given problem, and then they implement and tune the models. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that is used for a wide variety of applications.
Data Scientist
Data Scientist
Data Scientists collect and analyze data to find patterns that can be used to make decisions. They use their knowledge of statistics and programming to build models that can predict future events. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a powerful type of machine learning that is used in many fields of data science.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and implement software systems that can process and understand human language. They work with Machine Learning Engineers to create models that can recognize speech, translate text, and answer questions. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that is often used in natural language processing applications.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify patterns and trends. They work with businesses to make data-driven decisions. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that can be used to analyze data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to make investment decisions. They work with portfolio managers to develop and implement investment strategies. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that can be used to analyze financial data.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement software systems that can interpret visual data. They work with Machine Learning Engineers to create models that can identify objects, faces, and other patterns in images and videos. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that is often used in computer vision applications.
Academic Researcher
Academic Researcher
Academic Researchers conduct scientific research to expand the knowledge in their field. They publish their findings in academic journals and present their work at conferences. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that is used in many fields of research.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers create and maintain software systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. They work with Data Scientists and Machine Learning Engineers to develop and implement AI solutions to complex problems. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that is often used in AI systems.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. They work with clients to identify their investment goals and then develop and implement投资 strategies. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that can be used to analyze financial data.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks that could affect a company or organization. They work with management to develop and implement risk management strategies. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that can be used to identify and assess risks.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work with management to identify and analyze problems, and then they develop and implement solutions. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that can be used to solve complex business problems.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify and solve problems. They use data to analyze业务 processes and then develop and implement solutions. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that can be used to analyze business data.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots that can perform a variety of tasks, from manufacturing to healthcare. They work with Mechanical Engineers and Electrical Engineers to create robots that can move, sense, and interact with their environment. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding neural networks, a type of machine learning that is often used in robotics applications.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, deploying, testing, and maintaining software systems. They participate in every phase of the software development process, from gathering requirements to designing the system architecture. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation for understanding the fundamentals of neural networks, a type of machine learning often used in software systems.
Research Scientist
Research Scientist
A Research Scientist focuses on conducting scientific research, both applied and theoretical, to expand the knowledge in their field and to create new technologies or solutions to existing problems. They design and conduct experiments, analyze data, and develop new theories. An Introduction to Neural Networks with TensorFlow may be useful for this role because it provides a foundation that will help build your skills in machine learning, an increasingly important field for Research Scientists.
For more career information including salaries, visit:
OpenCourser.com/course/v7118t/introduction
Reading list
We've selected 13 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
Introduction to Neural Networks with TensorFlow.
Save
Is useful as a textbook and reference tool for practitioners of deep learning. It provides a comprehensive overview of the field, covering topics such as neural networks, convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
Provides a practical guide to machine learning with Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Comprehensive guide to TensorFlow, a popular open-source machine learning library. It covers a wide range of topics, including neural networks, convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
Practical guide to deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
Provides a comprehensive overview of machine learning with R. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a practical guide to deep reinforcement learning. It covers a wide range of topics, including deep Q-learning, policy gradients, and actor-critic methods.
Provides a comprehensive overview of generative adversarial networks (GANs). It covers a wide range of topics, including the theory behind GANs, the different types of GANs, and the applications of GANs.
Provides a concise overview of machine learning. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a practical guide to machine learning for hackers. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Save
Provides a critical look at data science. It covers a wide range of topics, including the ethical implications of data science, the challenges of data science, and the future of data science.
Provides a practical guide to data science for business. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a comprehensive overview of machine learning. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a practical guide to machine learning. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/v7118t/introduction
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Effort
3 weeks
Level
Intermediate
Via
Udacity
Instructors
Josh Bernhard
Michael Virgo
Mat Leonard
Andrew Paster
Jennifer Staab
Dan Romuald Mbanga
Cezanne Camacho
Sean Carrell
Jay Alammar
Luis Serrano
Juan Delgado
Language
English
Good to know
Taught by a team with substantial experience in industry, research, and education
Introduces the latest methods and techniques in neural networks and deep learning through interactive materials and hands-on labs
Focuses on the fundamentals of neural networks and their applications, suitable for beginners and those looking to strengthen their foundational skills
Covers advanced topics and real-world techniques in deep learning, making it ideal for intermediate learners seeking to advance their expertise
Teaches TensorFlow, a widely used industry-standard framework for deep learning
Emphasizes the practical aspects of implementing neural networks through a capstone project involving building a custom image classifier
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