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Introduction to Neural Networks with TensorFlow
Josh Bernhard ,
Michael Virgo,
Mat Leonard,
Andrew Paster,
Jennifer Staab,
Dan Romuald Mbanga,
Cezanne Camacho,
Sean Carrell,
Jay Alammar,
Luis Serrano,
and
Juan Delgado
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
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
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
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
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Save Introduction to Neural Networks with TensorFlow to your list so you can find it easily later:
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Activities
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We're preparing activities for
Introduction to Neural Networks with TensorFlow.
These are activities you can do either before, during, or after a course.
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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