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Andrew Ng,
Kian Katanforoosh,
and
Younes Bensouda Mourri
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
Read more
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
Read more
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/bxp0bz/neural
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What's inside
Syllabus
Introduction to Deep Learning
Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.
Read more
Syllabus
Introduction to Deep Learning
Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.
Read more
Neural Networks Basics
Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
Shallow Neural Networks
Build a neural network with one hidden layer, using forward propagation and backpropagation.
Deep Neural Networks
Analyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Establishes a solid understanding of neural networks and deep learning, which are crucial technologies in the field of artificial intelligence
Led by recognized instructors in the field of deep learning, including Andrew Ng, a pioneer in the development of deep learning
Concepts are presented with clear explanations and practical examples, making them accessible to learners with varying backgrounds
Covers key topics in deep learning, including neural network architectures, training algorithms, and applications in computer vision
Provides opportunities for hands-on practice through assignments and projects, allowing learners to apply the concepts they learn
May require a strong mathematical foundation, especially in linear algebra and calculus, which could pose a challenge for learners without this background
Save this course
Save Neural Networks and Deep Learning 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 and Deep Learning with these
activities:
Python Refresher
Show steps
Ensures familiarity with Python, the primary programming language used in deep learning.
Browse courses on
Python Programming
Show steps
-
Review basic Python concepts.
-
Practice writing simple Python scripts.
-
Complete a Python tutorial or online course.
Deep Learning Overview
Show steps
Solidifies understanding of key deep learning concepts and machine learning fundamentals.
Browse courses on
Deep Learning
Show steps
-
Review Coursera course description and materials.
-
Read introductory articles on deep learning and neural networks.
-
Take a practice quiz on deep learning basics.
Connect with Deep Learning Experts
Show steps
Provides guidance and support from experienced professionals in the field.
Browse courses on
Mentorship
Show steps
-
Attend industry events or conferences.
-
Join online communities or forums.
-
Reach out to researchers or practitioners in the field.
Six other activities
Expand to see all activities and additional details
Show all nine activities
TensorFlow Tutorial
Show steps
Provides practical experience in using TensorFlow, a popular deep learning framework.
Browse courses on
TensorFlow
Show steps
-
Follow a guided tutorial on TensorFlow.
-
Build a simple neural network using TensorFlow.
-
Experiment with different TensorFlow features.
Deep Learning Discussion Group
Show steps
Fosters collaboration, knowledge sharing, and deeper understanding through discussions.
Browse courses on
Peer Support
Show steps
-
Join an online discussion group dedicated to deep learning.
-
Participate in discussions, ask questions, and share insights.
-
Collaborate on projects or study sessions.
Neural Network Practice Exercises
Show steps
Reinforces understanding of neural network architectures and algorithms.
Browse courses on
Neural Networks
Show steps
-
Solve practice problems on neural network design.
-
Implement neural networks from scratch in Python.
-
Analyze the performance of different neural network models.
Deep Learning Blog or Tutorial
Show steps
Improves understanding and solidifies knowledge by teaching others.
Browse courses on
Content Creation
Show steps
-
Identify a specific topic in deep learning.
-
Research and gather information on the topic.
-
Write a blog post or create a tutorial explaining the topic.
-
Publish your content online and share it with others.
Deep Learning Hackathon
Show steps
Encourages problem-solving, innovation, and collaboration in a competitive setting.
Browse courses on
Hands-on Projects
Show steps
-
Find a deep learning hackathon or competition.
-
Form a team or work independently.
-
Develop and submit a deep learning solution.
-
Receive feedback and iterate on your solution.
Deep Learning Project
Show steps
Provides hands-on experience applying deep learning concepts to solve real-world problems.
Show steps
-
Identify a suitable deep learning project idea.
-
Collect and prepare the necessary data.
-
Design and implement a deep learning model.
-
Evaluate the model's performance and make improvements.
-
Present the project outcomes.
Python Refresher
Show steps
Ensures familiarity with Python, the primary programming language used in deep learning.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review basic Python concepts.
- Practice writing simple Python scripts.
- Complete a Python tutorial or online course.
Deep Learning Overview
Show steps
Solidifies understanding of key deep learning concepts and machine learning fundamentals.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Review Coursera course description and materials.
- Read introductory articles on deep learning and neural networks.
- Take a practice quiz on deep learning basics.
Connect with Deep Learning Experts
Show steps
Provides guidance and support from experienced professionals in the field.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Attend industry events or conferences.
- Join online communities or forums.
- Reach out to researchers or practitioners in the field.
Six other activities
Expand to see all activities and additional details
Show all nine activities
TensorFlow Tutorial
Show steps
Provides practical experience in using TensorFlow, a popular deep learning framework.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow a guided tutorial on TensorFlow.
- Build a simple neural network using TensorFlow.
- Experiment with different TensorFlow features.
Deep Learning Discussion Group
Show steps
Fosters collaboration, knowledge sharing, and deeper understanding through discussions.
Browse courses on
Peer Support
Show steps
Show steps
Show steps
- Join an online discussion group dedicated to deep learning.
- Participate in discussions, ask questions, and share insights.
- Collaborate on projects or study sessions.
Neural Network Practice Exercises
Show steps
Reinforces understanding of neural network architectures and algorithms.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Solve practice problems on neural network design.
- Implement neural networks from scratch in Python.
- Analyze the performance of different neural network models.
Deep Learning Blog or Tutorial
Show steps
Improves understanding and solidifies knowledge by teaching others.
Browse courses on
Content Creation
Show steps
Show steps
Show steps
- Identify a specific topic in deep learning.
- Research and gather information on the topic.
- Write a blog post or create a tutorial explaining the topic.
- Publish your content online and share it with others.
Deep Learning Hackathon
Show steps
Encourages problem-solving, innovation, and collaboration in a competitive setting.
Browse courses on
Hands-on Projects
Show steps
Show steps
Show steps
- Find a deep learning hackathon or competition.
- Form a team or work independently.
- Develop and submit a deep learning solution.
- Receive feedback and iterate on your solution.
Deep Learning Project
Show steps
Provides hands-on experience applying deep learning concepts to solve real-world problems.
Show steps
Show steps
Show steps
- Identify a suitable deep learning project idea.
- Collect and prepare the necessary data.
- Design and implement a deep learning model.
- Evaluate the model's performance and make improvements.
- Present the project outcomes.
Career center
Learners who complete Neural Networks and Deep Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve complex problems. They have a deep understanding of neural networks and deep learning, and use these techniques to create innovative solutions in various domains. This course offers a comprehensive introduction to neural networks and deep learning concepts, enabling you to excel in this role.
Speech Recognition Engineer
Speech Recognition Engineer
Speech Recognition Engineers develop and implement systems that allow computers to understand and transcribe spoken language. They often use deep learning techniques to build models for tasks such as speech recognition, speaker identification, and language translation. This course provides a practical foundation in deep learning and neural networks, equipping you with the knowledge and skills to succeed in speech recognition.
Data Scientist
Data Scientist
Data Scientists gather, analyze, and interpret complex data to extract meaningful insights and trends. They leverage machine learning algorithms, including deep neural networks, to uncover hidden patterns and predict future outcomes. This course provides a solid foundation in neural networks and deep learning, equipping you with the skills to build and apply these algorithms in real-world data science scenarios.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and implementing deep learning models for a wide range of applications, including computer vision, natural language processing, and speech recognition. This course provides a practical foundation in neural networks and deep learning, giving you the skills to excel in this highly specialized field.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop systems that enable computers to understand and generate human language. They use deep learning techniques to build models for tasks such as text classification, sentiment analysis, and machine translation. This course offers a solid foundation in neural networks and deep learning, providing you with the skills to excel in natural language processing.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers explore new methods and algorithms to advance the field of artificial intelligence. Deep learning is a key area of research, and researchers often use neural networks to develop innovative solutions to complex problems. This course provides a comprehensive introduction to neural networks and deep learning, giving you the foundation to contribute to the advancement of AI.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop algorithms and systems that allow computers to interpret and understand visual information. They often use deep learning techniques to build models for tasks such as object detection, image classification, and facial recognition. This course provides a strong foundation in deep learning and neural networks, equipping you with the knowledge and skills to succeed in computer vision.
Autonomous Vehicle Engineer
Autonomous Vehicle Engineer
Autonomous Vehicle Engineers develop and implement self-driving car systems. They often use deep learning techniques to build models for tasks such as object detection, lane keeping, and decision-making. This course provides a practical foundation in deep learning and neural networks, equipping you with the knowledge and skills to succeed in autonomous vehicle development.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and deploy intelligent robots that can perform complex tasks autonomously. They often use deep learning techniques to build models for tasks such as object manipulation, navigation, and decision-making. This course provides a practical foundation in deep learning and neural networks, giving you the skills to succeed in robotics.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. They often use deep learning techniques to build models for tasks such as forecasting, risk assessment, and market analysis. This course provides a solid foundation in deep learning and neural networks, equipping you with the skills to succeed as a Business Intelligence Analyst.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and patterns. They often use deep learning techniques to build models for tasks such as fraud detection, customer segmentation, and predictive analytics. This course provides a strong foundation in deep learning and neural networks, giving you the skills to succeed as a Data Analyst.
Systems Engineer
Systems Engineer
Systems Engineers design, develop, and maintain complex systems. They often use deep learning techniques to build models for tasks such as system optimization, fault detection, and predictive maintenance. This course provides a practical foundation in deep learning and neural networks, equipping you with the knowledge and skills to succeed as a Systems Engineer.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. They often use deep learning techniques to build models for tasks such as user experience analysis, market research, and product optimization. This course provides a practical foundation in deep learning and neural networks, giving you the skills to succeed as a Product Manager.
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software applications. They often use deep learning techniques to build models for tasks such as image recognition, natural language processing, and machine learning. This course provides a strong foundation in deep learning and neural networks, giving you the skills to succeed as a Software Engineer.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investments. They often use deep learning techniques to build models for tasks such as stock prediction, portfolio optimization, and risk assessment. This course provides a solid foundation in deep learning and neural networks, giving you the skills to succeed as a Financial Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/bxp0bz/neural
Reading list
We've selected ten 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 and Deep Learning.
Save
Provides a comprehensive overview of deep learning, covering the theoretical foundations, algorithms, and applications of deep learning. It valuable resource for students, researchers, and practitioners who want to learn more about deep learning.
Offers a mathematical perspective on machine learning and will be useful for those seeking a deeper understanding of the underlying algorithms.
Provides a gentle introduction to neural networks and deep learning, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students, researchers, and practitioners who want to learn more about neural networks and deep learning.
Save
Offers a hands-on guide to deep learning for practitioners.
Offers hands-on exercises that cover machine learning fundamentals and will aid in applying the concepts learned in this course.
Offers a practical and intuitive approach to understanding deep learning.
Provides a broad overview of neural networks and deep learning.
Provides a practical guide to deep learning with Python, covering the basics of deep learning, the Keras library, and applications of deep learning. It valuable resource for students, researchers, and practitioners who want to learn more about deep learning with Python.
Provides a practical guide to machine learning with Python, covering the basics of machine learning, the Scikit-Learn library, the Keras library, and the TensorFlow library. It valuable resource for students, researchers, and practitioners who want to learn more about machine learning with Python.
Provides a comprehensive overview of deep learning for natural language processing, covering the basics of deep learning, natural language processing, and applications of deep learning to natural language processing. It valuable resource for students, researchers, and practitioners who want to learn more about deep learning for natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bxp0bz/neural
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Effort
At the rate of 5 hours a week, it takes roughly 5 weeks to finish each course in the Specialization.
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Andrew Ng
Kian Katanforoosh
Younes Bensouda Mourri
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Bahasa Indonesia
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فارسی
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tiếng Việt
پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Deep Learning. It's comprised of five courses:
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
- Neural Networks and Deep Learning (viewing)
Good to know
Establishes a solid understanding of neural networks and deep learning, which are crucial technologies in the field of artificial intelligence
Led by recognized instructors in the field of deep learning, including Andrew Ng, a pioneer in the development of deep learning
Concepts are presented with clear explanations and practical examples, making them accessible to learners with varying backgrounds
Covers key topics in deep learning, including neural network architectures, training algorithms, and applications in computer vision
Provides opportunities for hands-on practice through assignments and projects, allowing learners to apply the concepts they learn
May require a strong mathematical foundation, especially in linear algebra and calculus, which could pose a challenge for learners without this background
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