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Sebastian Thrun,
Cezanne Camacho,
Jay Alammar,
Alexis Cook,
Luis Serrano,
Juan Delgado,
and
Ortal Arel
Review how neural networks turn an input into an output and how they monitor errors as they train. This section will also cover methods to avoid overfitting your data.
Register for this course and see more details by visiting:
OpenCourser.com/course/419jho/review
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What's inside
Syllabus
Short introduction to neural networks: how they train by doing a feedforward pass then performing backpropagation.
Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
Learn how to use PyTorch for building deep learning models
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers fundamentals such as feedforward passes, backpropagation, and overfitting prevention
Utilizes PyTorch, a popular deep learning framework, for hands-on experience
Taught by instructors with expertise in the field of neural networks
Suitable for learners seeking to strengthen their foundation in neural networks
Save this course
Save Review: Training A Neural Network 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 Review: Training A Neural Network with these
activities:
Read: Deep Learning with Python (2nd Edition)
Show steps
Start to build a foundation in Deep Learning before starting the course. Focus on Concepts such as TensorFlow and Keras
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Understand the basics of deep learning.
-
Learn how to use TensorFlow and Keras to build deep learning models.
-
Create a few deep learning projects of your own.
Organize Your Course Notes
Show steps
Go through lecture materials and extract key points in your own words. This will help strengthen your understanding and retention of the concepts.
Show steps
-
Review your lecture notes.
-
Identify the key points in each lecture.
-
Summarize the key points in your own words.
-
Organize your notes into a logical structure.
Form a Study Group
Show steps
Find other students enrolled in the course to form a study group. Meet regularly to review the material, discuss the concepts, and help one another.
Show steps
-
Reach out to other students in the course.
-
Find a few students who are interested in forming a study group.
-
Set up a regular meeting time and place.
-
Use the study group to review the material, discuss the concepts, and help one another.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Backpropagation
Show steps
Practice implementing Backpropagation to train neural networks by hand. This will enhance your understanding of neural network training.
Browse courses on
Backpropagation
Show steps
-
Implement a simple feed-forward neural network.
-
Implement the backpropagation algorithm.
-
Use backpropagation to train the neural network on a simple dataset.
Follow a PyTorch Tutorial for Building a Convolutional Neural Network (CNN)
Show steps
Follow a PyTorch tutorial to build a Convolutional Neural Network (CNN). CNNs are used for image recognition and object detection.
Browse courses on
PyTorch
Show steps
-
Find a PyTorch tutorial on building a CNN.
-
Follow the tutorial step-by-step.
-
Experiment with different CNN architectures and datasets.
Build a Neural Network from Scratch in PyTorch
Show steps
Create a neural network from scratch using PyTorch. This will help you develop a deeper understanding of the inner workings of neural networks.
Browse courses on
PyTorch
Show steps
-
Create a new PyTorch project.
-
Define the neural network architecture.
-
Implement the forward and backward passes.
-
Train the neural network on a dataset.
Participate in a Kaggle Competition on Neural Networks
Show steps
Apply your neural network skills by competing in a Kaggle competition.
Browse courses on
Kaggle
Show steps
-
Find a Kaggle competition that uses neural networks.
-
Download and explore the competition dataset.
-
Build a neural network model for the competition.
-
Submit your model to the competition.
Mentor a Beginner in Neural Networks
Show steps
Share your knowledge and help others to learn about neural networks. This will aid you in reinforcing your understanding.
Browse courses on
Mentoring
Show steps
-
Find a beginner who is interested in learning about neural networks.
-
Meet with the beginner regularly to answer their questions and provide guidance.
-
Share resources and materials on neural networks with the beginner.
Read: Deep Learning with Python (2nd Edition)
Show steps
Start to build a foundation in Deep Learning before starting the course. Focus on Concepts such as TensorFlow and Keras
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Understand the basics of deep learning.
- Learn how to use TensorFlow and Keras to build deep learning models.
- Create a few deep learning projects of your own.
Organize Your Course Notes
Show steps
Go through lecture materials and extract key points in your own words. This will help strengthen your understanding and retention of the concepts.
Show steps
Show steps
Show steps
- Review your lecture notes.
- Identify the key points in each lecture.
- Summarize the key points in your own words.
- Organize your notes into a logical structure.
Form a Study Group
Show steps
Find other students enrolled in the course to form a study group. Meet regularly to review the material, discuss the concepts, and help one another.
Show steps
Show steps
Show steps
- Reach out to other students in the course.
- Find a few students who are interested in forming a study group.
- Set up a regular meeting time and place.
- Use the study group to review the material, discuss the concepts, and help one another.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Backpropagation
Show steps
Practice implementing Backpropagation to train neural networks by hand. This will enhance your understanding of neural network training.
Browse courses on
Backpropagation
Show steps
Show steps
Show steps
- Implement a simple feed-forward neural network.
- Implement the backpropagation algorithm.
- Use backpropagation to train the neural network on a simple dataset.
Follow a PyTorch Tutorial for Building a Convolutional Neural Network (CNN)
Show steps
Follow a PyTorch tutorial to build a Convolutional Neural Network (CNN). CNNs are used for image recognition and object detection.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Find a PyTorch tutorial on building a CNN.
- Follow the tutorial step-by-step.
- Experiment with different CNN architectures and datasets.
Build a Neural Network from Scratch in PyTorch
Show steps
Create a neural network from scratch using PyTorch. This will help you develop a deeper understanding of the inner workings of neural networks.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Create a new PyTorch project.
- Define the neural network architecture.
- Implement the forward and backward passes.
- Train the neural network on a dataset.
Participate in a Kaggle Competition on Neural Networks
Show steps
Apply your neural network skills by competing in a Kaggle competition.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Find a Kaggle competition that uses neural networks.
- Download and explore the competition dataset.
- Build a neural network model for the competition.
- Submit your model to the competition.
Mentor a Beginner in Neural Networks
Show steps
Share your knowledge and help others to learn about neural networks. This will aid you in reinforcing your understanding.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Find a beginner who is interested in learning about neural networks.
- Meet with the beginner regularly to answer their questions and provide guidance.
- Share resources and materials on neural networks with the beginner.
Career center
Learners who complete Review: Training A Neural Network will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists are in charge of analyzing and interpreting large sets of data, extracting insights and building models to predict future trends and outcomes. This course's coverage of neural networks, backpropagation, and techniques to avoid overfitting directly contribute to a data scientist's core skillset. This course helps build a foundation for success in data science, particularly for those interested in building and applying neural networks to real-world data.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers build, deploy, and maintain machine learning models. They work closely with data scientists to bring models into production. Understanding neural networks is crucial for machine learning engineers, as they are a fundamental building block of many machine learning models. This course provides a solid foundation in neural networks, backpropagation, and overfitting avoidance techniques, preparing learners for a successful career as a machine learning engineer.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. Neural networks are increasingly being used to develop new and innovative software applications, and knowledge of neural networks is becoming increasingly valuable for software engineers. This course provides a solid foundation in neural networks, backpropagation, and overfitting avoidance techniques, preparing learners for a successful career in software engineering.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and statistical models to solve complex problems in business and industry. Neural networks are increasingly being used in operations research to develop new and innovative solutions to real-world problems. This course provides a solid foundation in neural networks, backpropagation, and overfitting avoidance techniques, preparing learners for a successful career as an operations research analyst.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural language processing engineers develop and maintain software that enables computers to understand and process human language. Neural networks are a fundamental part of many natural language processing applications, such as machine translation and text summarization. This course provides a solid foundation in neural networks, backpropagation, and overfitting avoidance techniques, preparing learners for a successful career as a natural language processing engineer.
Computer Vision Engineer
Computer Vision Engineer
Computer vision engineers develop and maintain software that enables computers to see and interpret images. Neural networks are a fundamental part of many computer vision applications, such as object detection and recognition, and face recognition. This course provides a solid foundation in neural networks, backpropagation, and overfitting avoidance techniques, preparing learners for a successful career as a computer vision engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical models to analyze financial data and make investment decisions. Neural networks are increasingly being used in quantitative finance to develop trading strategies and risk models. This course provides a solid foundation in neural networks, backpropagation, and overfitting avoidance techniques, preparing learners for a successful career as a quantitative analyst.
Robotics Engineer
Robotics Engineer
Robotics engineers design, develop, and maintain robots, giving them the ability to sense, reason, and act. Neural networks are increasingly being used in robotics to develop new and innovative robot behaviors. This course provides a solid foundation in neural networks, backpropagation, and overfitting avoidance techniques, preparing learners for a successful career as a robotics engineer.
Data Analyst
Data Analyst
Data analysts collect, clean, and analyze data to derive insights and inform decision-making. Neural networks can be used to perform complex data analysis tasks, such as anomaly detection and predictive modeling. This course provides a solid foundation in neural networks, backpropagation, and overfitting avoidance techniques, preparing learners for a successful career as a data analyst.
Financial Analyst
Financial Analyst
Financial analysts analyze financial data and make investment recommendations. Neural networks can be used to analyze financial data and develop trading strategies. This course provides a basic understanding of neural networks, which may be useful for financial analysts who want to understand how neural networks can be used to improve investment decisions.
Business Analyst
Business Analyst
Business analysts help organizations improve their business processes and make better decisions. Neural networks can be used to analyze data and develop predictive models to support decision-making. This course provides a basic understanding of neural networks, which may be useful for business analysts who want to understand how neural networks can be used to improve business outcomes.
Project Manager
Project Manager
Project managers are responsible for planning and executing projects. Neural networks can be used to analyze project data and develop predictive models to support decision-making. This course provides a basic understanding of neural networks, which may be useful for project managers who want to understand how neural networks can be used to improve project outcomes.
Product Manager
Product Manager
Product managers are responsible for developing and managing new products. Neural networks can be used to analyze customer data and develop new product features and offerings. This course provides a basic understanding of neural networks, which may be useful for product managers who want to understand how neural networks can be used to improve product development.
Consultant
Consultant
Consultants provide advice and support to organizations on a variety of business issues. Neural networks can be used to analyze data and develop predictive models to support decision-making. This course provides a basic understanding of neural networks, which may be useful for consultants who want to understand how neural networks can be used to improve consulting outcomes.
Market Researcher
Market Researcher
Market researchers collect and analyze data about consumer behavior and market trends. Neural networks can be used to analyze market data and develop predictive models to support decision-making. This course provides a basic understanding of neural networks, which may be useful for market researchers who want to understand how neural networks can be used to improve market research outcomes.
For more career information including salaries, visit:
OpenCourser.com/course/419jho/review
Reading list
We've selected 12 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
Review: Training A Neural Network.
Save
Provides a comprehensive overview of deep learning, covering both the theoretical and practical aspects of the field. It valuable resource for both beginners and experienced practitioners.
Provides a practical guide to machine learning using Python. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Provides a practical guide to deep learning using Python. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Save
Provides a comprehensive overview of machine learning. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Provides a practical guide to machine learning for hackers. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Provides a practical guide to machine learning using R. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Save
Provides a comprehensive overview of data mining. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Save
Provides a comprehensive overview of neural networks. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics from data preprocessing to model evaluation. It valuable resource for both beginners and experienced practitioners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/419jho/review
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Time
210 hours
Level
Advanced
Via
Udacity
Instructors
Sebastian Thrun
Cezanne Camacho
Jay Alammar
Alexis Cook
Luis Serrano
Juan Delgado
Ortal Arel
Language
English
Good to know
Covers fundamentals such as feedforward passes, backpropagation, and overfitting prevention
Utilizes PyTorch, a popular deep learning framework, for hands-on experience
Taught by instructors with expertise in the field of neural networks
Suitable for learners seeking to strengthen their foundation in neural networks
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