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Mat Leonard,
Andrew Paster,
Jennifer Staab,
Luis Serrano,
Juan Delgado,
Juno Lee,
Mike Yi,
Grant Sanderson,
and
Ortal Arel
This course on neural networks explains how algorithms inspired by the human brain operate and puts to use those concepts when designing neural networks to solve particular problems.
Register for this course and see more details by visiting:
OpenCourser.com/course/rtz8u5/neural
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What's inside
Syllabus
Welcome to Neural Networks
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.
Read more
Syllabus
Welcome to Neural Networks
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.
Read more
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 PyTorch for building deep learning models.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches neural networks, which is standard in industry and academia
Builds a strong foundation for beginners in deep learning
Introduces the gradient descent and backpropagation algorithms
Demonstrates how to use PyTorch for deep learning models
Offers hands-on labs and interactive materials
Save this course
Save Neural Networks - AI Programming with Python 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 Neural Networks - AI Programming with Python with these
activities:
Review the fundamentals of calculus and linear algebra
Show steps
Strengthen your foundation in calculus and linear algebra, crucial for understanding neural network concepts.
Browse courses on
Calculus
Show steps
-
Revisit key concepts in calculus, such as derivatives and integrals
-
Review basic linear algebra operations and matrix theory
-
Practice solving problems involving both calculus and linear algebra
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Gain a comprehensive understanding of deep learning theory and applications through this foundational text.
View
Deep Learning
on Amazon
Show steps
-
Read and understand the book's introduction and overview of deep learning concepts
-
Study specific chapters relevant to the course curriculum
-
Work through the book's exercises to test your comprehension
Solve gradient descent problems
Show steps
Improve your understanding of gradient descent and its applications in neural network training.
Browse courses on
Gradient Descent
Show steps
-
Study the theory behind gradient descent
-
Practice solving equations and optimization problems using gradient descent
-
Implement gradient descent algorithms in Python
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice implementing backpropagation
Show steps
Reinforce your understanding of the backpropagation algorithm, essential for designing neural networks.
Browse courses on
Backpropagation
Show steps
-
Implement a basic feedforward neural network in Python
-
Define a simple loss function and compute its gradient
-
Use the gradient to update the network's weights and biases
-
Train the network on a toy dataset
Tutorial on PyTorch for Deep Learning
Show steps
Expand your knowledge of PyTorch, a popular deep learning framework, to enhance your model-building skills.
Browse courses on
PyTorch
Show steps
-
Install PyTorch and its dependencies
-
Create a simple neural network model in PyTorch
-
Train and evaluate the model using PyTorch's built-in functions
-
Explore additional resources and tutorials on PyTorch
Design and implement a neural network for a specific problem
Show steps
Apply your knowledge to a real-world scenario by developing and deploying a neural network for a specific task.
Show steps
-
Identify a problem or dataset suitable for a neural network solution
-
Design the architecture and parameters of the network
-
Train and evaluate the network on the chosen dataset
-
Deploy and test the network in a real-world application
-
Optimize and refine the network's performance based on testing results
Review the fundamentals of calculus and linear algebra
Show steps
Strengthen your foundation in calculus and linear algebra, crucial for understanding neural network concepts.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Revisit key concepts in calculus, such as derivatives and integrals
- Review basic linear algebra operations and matrix theory
- Practice solving problems involving both calculus and linear algebra
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Gain a comprehensive understanding of deep learning theory and applications through this foundational text.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read and understand the book's introduction and overview of deep learning concepts
- Study specific chapters relevant to the course curriculum
- Work through the book's exercises to test your comprehension
Solve gradient descent problems
Show steps
Improve your understanding of gradient descent and its applications in neural network training.
Browse courses on
Gradient Descent
Show steps
Show steps
Show steps
- Study the theory behind gradient descent
- Practice solving equations and optimization problems using gradient descent
- Implement gradient descent algorithms in Python
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice implementing backpropagation
Show steps
Reinforce your understanding of the backpropagation algorithm, essential for designing neural networks.
Browse courses on
Backpropagation
Show steps
Show steps
Show steps
- Implement a basic feedforward neural network in Python
- Define a simple loss function and compute its gradient
- Use the gradient to update the network's weights and biases
- Train the network on a toy dataset
Tutorial on PyTorch for Deep Learning
Show steps
Expand your knowledge of PyTorch, a popular deep learning framework, to enhance your model-building skills.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Install PyTorch and its dependencies
- Create a simple neural network model in PyTorch
- Train and evaluate the model using PyTorch's built-in functions
- Explore additional resources and tutorials on PyTorch
Design and implement a neural network for a specific problem
Show steps
Apply your knowledge to a real-world scenario by developing and deploying a neural network for a specific task.
Show steps
Show steps
Show steps
- Identify a problem or dataset suitable for a neural network solution
- Design the architecture and parameters of the network
- Train and evaluate the network on the chosen dataset
- Deploy and test the network in a real-world application
- Optimize and refine the network's performance based on testing results
Career center
Learners who complete Neural Networks - AI Programming with Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models to solve real-world problems. This course on neural networks can help you succeed as a Machine Learning Engineer by providing you with a solid foundation in the fundamentals of deep learning and neural networks. You'll learn how to implement gradient descent and backpropagation in Python, which are essential skills for building and training neural networks. Additionally, you'll learn several techniques to improve the training of neural networks, which will help you to develop more accurate and efficient models.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing large amounts of data to extract meaningful insights. This course on neural networks can help you succeed as a Data Scientist by providing you with a solid foundation in the fundamentals of deep learning and neural networks. You'll learn how to implement gradient descent and backpropagation in Python, which are essential skills for building and training neural networks. Additionally, you'll learn several techniques to improve the training of neural networks, which will help you to develop more accurate and efficient models.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, build, and deploy artificial intelligence systems. This course on neural networks can help you succeed as an Artificial Intelligence Engineer by providing you with a solid foundation in the fundamentals of deep learning and neural networks. You'll learn how to implement gradient descent and backpropagation in Python, which are essential skills for building and training neural networks. Additionally, you'll learn several techniques to improve the training of neural networks, which will help you to develop more accurate and efficient models.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design, build, and deploy computer vision systems to solve real-world problems. This course on neural networks can help you succeed as a Computer Vision Engineer by providing you with a solid foundation in the fundamentals of deep learning and neural networks. You'll learn how to implement gradient descent and backpropagation in Python, which are essential skills for building and training neural networks. Additionally, you'll learn several techniques to improve the training of neural networks, which will help you to develop more accurate and efficient models.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design, build, and deploy natural language processing systems to solve real-world problems. This course on neural networks can help you succeed as a Natural Language Processing Engineer by providing you with a solid foundation in the fundamentals of deep learning and neural networks. You'll learn how to implement gradient descent and backpropagation in Python, which are essential skills for building and training neural networks. Additionally, you'll learn several techniques to improve the training of neural networks, which will help you to develop more accurate and efficient models.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and deploy robots to solve real-world problems. This course on neural networks can help you succeed as a Robotics Engineer by providing you with a solid foundation in the fundamentals of deep learning and neural networks. You'll learn how to implement gradient descent and backpropagation in Python, which are essential skills for building and training neural networks. Additionally, you'll learn several techniques to improve the training of neural networks, which will help you to develop more accurate and efficient models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract meaningful insights. This course on neural networks may be helpful for Data Analysts who want to develop more sophisticated data analysis techniques. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to develop more accurate and efficient data analysis models.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course on neural networks may be helpful for Financial Analysts who want to develop more sophisticated investment analysis techniques. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to develop more accurate and efficient investment analysis models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course on neural networks may be helpful for Quantitative Analysts who want to develop more sophisticated financial analysis techniques. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to develop more accurate and efficient financial analysis models.
Biostatistician
Biostatistician
Biostatisticians use statistical techniques to analyze biological and medical data. This course on neural networks may be helpful for Biostatisticians who want to develop more sophisticated statistical analysis techniques. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to develop more accurate and efficient statistical analysis models.
Business Analyst
Business Analyst
Business Analysts use data and analysis to solve business problems. This course on neural networks may be helpful for Business Analysts who want to develop more sophisticated business analysis techniques. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to develop more accurate and efficient business analysis models.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. This course on neural networks may be helpful for Market Researchers who want to develop more sophisticated market research techniques. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to develop more accurate and efficient market research models.
Statistician
Statistician
Statisticians use statistical techniques to analyze data. This course on neural networks may be helpful for Statisticians who want to develop more sophisticated statistical analysis techniques. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to develop more accurate and efficient statistical analysis models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve business problems. This course on neural networks may be helpful for Operations Research Analysts who want to develop more sophisticated business analysis techniques. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to develop more accurate and efficient business analysis models.
Software Engineer
Software Engineer
Software Engineers design, build, and deploy software applications. This course on neural networks may be helpful for Software Engineers who want to develop artificial intelligence applications. You'll learn the fundamentals of deep learning and neural networks, and you'll learn how to implement gradient descent and backpropagation in Python. This knowledge can help you to build more sophisticated and intelligent software applications.
For more career information including salaries, visit:
OpenCourser.com/course/rtz8u5/neural
Reading list
We've selected nine 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
Neural Networks - AI Programming with Python.
Save
Provides a comprehensive overview of deep learning, covering the fundamental concepts, algorithms, and applications. It valuable reference for both beginners and experienced practitioners.
Provides a hands-on introduction to machine learning using Python. It covers a wide range of topics, including neural networks, deep learning, and natural language processing.
Provides a comprehensive guide to deep learning using Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Save
Provides a comprehensive overview of neural networks. It good reference for both beginners and experienced practitioners.
Save
Provides a comprehensive overview of machine learning. It good reference for both beginners and experienced practitioners.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, including text classification, machine translation, and question answering.
Provides a comprehensive overview of artificial intelligence. It good starting point for those who are new to the field.
Provides a comprehensive overview of machine learning. It good reference for both beginners and experienced practitioners.
Provides a comprehensive overview of deep reinforcement learning. It covers a wide range of topics, including Markov decision processes, value function approximation, and policy gradient methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rtz8u5/neural
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Time
775 hours
Level
Introductory
Via
Udacity
Instructors
Mat Leonard
Andrew Paster
Jennifer Staab
Luis Serrano
Juan Delgado
Juno Lee
Mike Yi
Grant Sanderson
Ortal Arel
Language
English
Good to know
Teaches neural networks, which is standard in industry and academia
Builds a strong foundation for beginners in deep learning
Introduces the gradient descent and backpropagation algorithms
Demonstrates how to use PyTorch for deep learning models
Offers hands-on labs and interactive materials
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