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Mat Leonard,
Parnian Barekatain,
Eddy Shyu,
Brok Bucholtz,
Elizabeth Otto Hamel,
Cindy Lin,
Cezanne Camacho,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Register for this course and see more details by visiting:
OpenCourser.com/course/8vt1jc/deep
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What's inside
Syllabus
In this lesson, Luis will teach you the foundations of deep learning and neural networks. You'll also implement gradient descent and backpropagation in python, right here in the classroom!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers deep learning and neural networks, highly relevant to industry
Emphasizes the core concepts of artificial intelligence
Led by Luis Serrano, an experienced instructor in deep learning
Teaches both theoretical and practical aspects of deep learning
Provides step-by-step guidance for implementing gradient descent and backpropagation
Save this course
Save Deep Learning to your list so you can find it easily later:
Save
Save
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 with these
activities:
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, Aaron Courville
Show steps
Expand your theoretical knowledge by reading the seminal book on deep learning.
View
Deep Learning
on Amazon
Show steps
-
Read the book thoroughly
-
Take notes and highlight important concepts
-
Discuss the book with classmates or online forums
Review Linear Algebra and Calculus
Show steps
Strengthen your mathematical foundation by reviewing linear algebra and calculus.
Browse courses on
Linear Algebra
Show steps
-
Review key concepts in linear algebra
-
Refresh your knowledge of differential and integral calculus
-
Solve practice problems to test your understanding
Review Python for Neural Networks
Show steps
Boost your understanding of Python, a fundamental requirement for neural networks and machine learning.
Browse courses on
Python
Show steps
-
Revisit basic Python syntax and data structures
-
Review object-oriented programming concepts in Python
-
Practice writing simple Python scripts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement Gradient Descent in Python
Show steps
Solidify your understanding of gradient descent by implementing it from scratch in Python.
Browse courses on
Gradient Descent
Show steps
-
Understand the mathematical concepts behind gradient descent
-
Translate the mathematical equations into Python code
-
Test your implementation on a simple dataset
Follow tutorials on Backpropagation
Show steps
Deepen your knowledge of backpropagation by following guided tutorials.
Browse courses on
Backpropagation
Show steps
-
Find reputable online tutorials on backpropagation
-
Go through the tutorials step-by-step
-
Implement the backpropagation algorithm in Python
Build a Neural Network using TensorFlow
Show steps
Apply your knowledge by building a neural network using TensorFlow, gaining hands-on experience.
Browse courses on
TensorFlow
Show steps
-
Choose a dataset and define the problem
-
Design the neural network architecture
-
Implement the neural network in TensorFlow
-
Train and evaluate the neural network
Participate in a Kaggle competition on Deep Learning
Show steps
Challenge yourself by participating in a Kaggle competition, testing your skills and expanding your knowledge.
Browse courses on
Kaggle
Show steps
-
Find an appropriate Kaggle competition
-
Download the dataset and familiarize yourself with it
-
Develop a solution using deep learning techniques
-
Submit your solution and track your progress
Attend industry conferences on Deep Learning
Show steps
Expand your network and stay updated on the latest trends in deep learning.
Browse courses on
Deep Learning
Show steps
-
Find industry conferences related to deep learning
-
Register and attend the conferences
-
Network with professionals and experts in the field
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, Aaron Courville
Show steps
Expand your theoretical knowledge by reading the seminal book on deep learning.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly
- Take notes and highlight important concepts
- Discuss the book with classmates or online forums
Review Linear Algebra and Calculus
Show steps
Strengthen your mathematical foundation by reviewing linear algebra and calculus.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review key concepts in linear algebra
- Refresh your knowledge of differential and integral calculus
- Solve practice problems to test your understanding
Review Python for Neural Networks
Show steps
Boost your understanding of Python, a fundamental requirement for neural networks and machine learning.
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisit basic Python syntax and data structures
- Review object-oriented programming concepts in Python
- Practice writing simple Python scripts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement Gradient Descent in Python
Show steps
Solidify your understanding of gradient descent by implementing it from scratch in Python.
Browse courses on
Gradient Descent
Show steps
Show steps
Show steps
- Understand the mathematical concepts behind gradient descent
- Translate the mathematical equations into Python code
- Test your implementation on a simple dataset
Follow tutorials on Backpropagation
Show steps
Deepen your knowledge of backpropagation by following guided tutorials.
Browse courses on
Backpropagation
Show steps
Show steps
Show steps
- Find reputable online tutorials on backpropagation
- Go through the tutorials step-by-step
- Implement the backpropagation algorithm in Python
Build a Neural Network using TensorFlow
Show steps
Apply your knowledge by building a neural network using TensorFlow, gaining hands-on experience.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Choose a dataset and define the problem
- Design the neural network architecture
- Implement the neural network in TensorFlow
- Train and evaluate the neural network
Participate in a Kaggle competition on Deep Learning
Show steps
Challenge yourself by participating in a Kaggle competition, testing your skills and expanding your knowledge.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Find an appropriate Kaggle competition
- Download the dataset and familiarize yourself with it
- Develop a solution using deep learning techniques
- Submit your solution and track your progress
Attend industry conferences on Deep Learning
Show steps
Expand your network and stay updated on the latest trends in deep learning.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find industry conferences related to deep learning
- Register and attend the conferences
- Network with professionals and experts in the field
Career center
Learners who complete Deep Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists design and implement data collection, cleaning, analysis, and modeling strategies to extract meaningful insights from large datasets. They use their deep understanding of machine learning, statistics, and programming languages to uncover trends, patterns, and anomalies in data. This course in Deep Learning is foundational for Data Scientists, providing them with a solid understanding of the principles underlying deep learning models, which are increasingly used in data science applications.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models for various applications, such as natural language processing, computer vision, and speech recognition. This course in Deep Learning is essential for Machine Learning Engineers, as deep learning models are widely used in these domains and require a strong theoretical foundation and practical skills.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers develop systems that can automatically recognize and transcribe human speech. Deep learning has made significant contributions to speech recognition accuracy and efficiency. This course provides Speech Recognition Engineers with a deep understanding of the principles and techniques of deep learning, enabling them to design and implement state-of-the-art speech recognition systems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop computer vision systems that can interpret and understand visual data, such as images and videos. Deep learning plays a central role in computer vision tasks such as object detection, image segmentation, and facial recognition. This course provides Computer Vision Engineers with a deep understanding of the principles and algorithms of deep learning, enabling them to design and implement powerful computer vision systems.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and implement AI systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. Deep learning is a fundamental technology in AI, and this course provides AI Engineers with a solid foundation in the principles and techniques of deep learning, enabling them to design and implement effective AI systems.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop systems that can understand, interpret, and generate human language. Deep learning has revolutionized NLP tasks such as machine translation, text classification, and question answering. This course provides NLP Engineers with a strong foundation in deep learning, empowering them to develop advanced NLP systems that can handle complex language-based tasks.
Deep Learning Researcher
Deep Learning Researcher
Deep Learning Researchers push the boundaries of deep learning theory and develop new algorithms, models, and applications of deep learning. This course provides Deep Learning Researchers with a strong foundation in the fundamental principles of deep learning, enabling them to contribute to the advancement of the field and develop innovative deep learning solutions.
Computational Neuroscientist
Computational Neuroscientist
Computational Neuroscientists use computational models and simulations to study the structure and function of the nervous system. Deep learning has provided powerful new tools for understanding brain function and developing new treatments for neurological disorders. This course in Deep Learning may be useful for Computational Neuroscientists who wish to incorporate deep learning into their research or develop new deep learning-based approaches to study the nervous system.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. Deep learning is increasingly used in robotics for tasks such as object recognition, path planning, and decision-making. This course in Deep Learning may be useful for Robotics Engineers who wish to incorporate deep learning into their work and develop new deep learning-based approaches to robotics.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Deep learning has found applications in quantitative finance, such as stock price prediction and risk assessment. This course in Deep Learning may be useful for Quantitative Analysts who wish to explore the applications of deep learning in finance and develop new deep learning-based approaches to financial analysis.
Bioinformatician
Bioinformatician
Bioinformaticians use computational tools and techniques to analyze biological data, such as DNA sequences and gene expression profiles. Deep learning has seen growing applications in bioinformatics, such as disease diagnosis and drug discovery. This course in Deep Learning may be useful for Bioinformatics who wish to learn about the principles and applications of deep learning in bioinformatics and develop new deep learning-based approaches to biological data analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Deep learning is increasingly used in software development for tasks such as image recognition, natural language processing, and fraud detection. This course in Deep Learning may be useful for Software Engineers who wish to incorporate deep learning into their work and develop new deep learning-based software applications.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract meaningful insights. Deep learning is increasingly used in data analysis for tasks such as anomaly detection, fraud detection, and customer segmentation. This course in Deep Learning may be useful for Data Analysts who wish to incorporate deep learning into their work and develop new deep learning-based approaches to data analysis.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations on investments and financial decisions. Deep learning is increasingly used in financial analysis for tasks such as stock price prediction, risk assessment, and fraud detection. This course in Deep Learning may be useful for Financial Analysts who wish to incorporate deep learning into their work and develop new deep learning-based approaches to financial analysis.
Business Analyst
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency and effectiveness. Deep learning is increasingly used in business analysis for tasks such as market segmentation, customer churn prediction, and risk assessment. This course in Deep Learning may be useful for Business Analysts who wish to incorporate deep learning into their work and develop new deep learning-based approaches to business analysis.
For more career information including salaries, visit:
OpenCourser.com/course/8vt1jc/deep
Reading list
We've selected seven 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.
Provides a comprehensive overview of deep learning for social good. It valuable reference for both beginners and experienced practitioners.
Provides a comprehensive overview of deep learning for physics. It valuable reference for both beginners and experienced practitioners.
Provides a comprehensive overview of deep learning for natural language processing. It valuable reference for both beginners and experienced practitioners.
Save
Provides a comprehensive overview of deep learning, from its foundations to its applications. It valuable reference for both beginners and experienced practitioners.
Provides a comprehensive overview of deep learning for computer vision. It valuable reference for both beginners and experienced practitioners.
Provides a practical introduction to deep learning with Python. It valuable resource for beginners who want to learn about the basics of deep learning.
Provides a practical introduction to machine learning, with a focus on deep learning. It valuable resource for beginners who want to learn about the basics of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8vt1jc/deep
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Time
300 hours
Level
Advanced
Via
Udacity
Instructors
Mat Leonard
Parnian Barekatain
Eddy Shyu
Brok Bucholtz
Elizabeth Otto Hamel
Cindy Lin
Cezanne Camacho
Arpan Chakraborty
Luis Serrano
Juan Delgado
Language
English
Good to know
Covers deep learning and neural networks, highly relevant to industry
Emphasizes the core concepts of artificial intelligence
Led by Luis Serrano, an experienced instructor in deep learning
Teaches both theoretical and practical aspects of deep learning
Provides step-by-step guidance for implementing gradient descent and backpropagation
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