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Neural Networks and Deep Learning

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.

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.

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.

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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

Traffic lights

Read about what's good
what should give you pause
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

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Reviews summary

Foundational deep learning introduction

According to learners, this course provides a solid and accessible foundation for understanding the core concepts of neural networks and deep learning. Students frequently praise the clear explanations provided by the instructor, finding complex topics broken down effectively. The included programming assignments are widely regarded as essential and highly beneficial for applying the theoretical knowledge and gaining practical experience. While the course delves into the necessary mathematical underpinnings, some reviewers note that a prior grasp of linear algebra and calculus is advantageous. Overall, it is seen as an excellent starting point for anyone serious about entering the AI and deep learning field.
Familiarity with math is beneficial.
"While explained, having a solid understanding of linear algebra and calculus makes the math sections much easier."
"The mathematical derivations are necessary, but be prepared to review concepts if your math skills are rusty."
"I recommend brushing up on matrix operations before starting if you're not comfortable with them."
Accessible introduction for newcomers.
"As someone new to the field, I found this course to be the perfect introduction."
"It assumes no prior knowledge of deep learning, but a basic programming background is helpful."
"Great course if you are just starting out and want to build a strong theoretical foundation."
Practical exercises solidify theoretical learning.
"The programming assignments were the highlight for me; they really helped solidify the concepts learned in lectures."
"Working through the code examples and assignments is crucial for understanding how to apply the theory."
"I learned so much from the hands-on coding labs, they are well-designed and reinforce everything."
Instructor explains complex ideas simply.
"Andrew Ng is fantastic at explaining complex concepts in a simple and intuitive way."
"The lectures are incredibly clear and easy to follow, even when the material is challenging."
"His teaching style made the theory behind neural networks click for me."
Provides a comprehensive base in deep learning.
"This course provides a very strong foundation for understanding the fundamental concepts behind neural networks."
"I finally feel like I have a solid grasp on the basics of deep learning thanks to this course."
"Excellent starting point, covers the essentials you need before moving to more advanced topics."

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
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
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
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
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
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
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
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
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
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.

Career center

Learners who complete Neural Networks and Deep Learning will develop knowledge and skills that may be useful to these careers:
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Machine 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.
See salaries and explore the career path for Deep Learning 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.
See salaries and explore the career path for Computer Vision 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.
See salaries and explore the career path for Natural Language Processing 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.
See salaries and explore the career path for Speech Recognition Engineer
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.
See salaries and explore the career path for Artificial Intelligence Researcher
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.
See salaries and explore the career path for Robotics 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.
See salaries and explore the career path for Autonomous Vehicle Engineer
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.
See salaries and explore the career path for Data 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.
See salaries and explore the career path for Business Intelligence Analyst
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.
See salaries and explore the career path for Product Manager
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.
See salaries and explore the career path for Software 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.
See salaries and explore the career path for Systems Engineer
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.
See salaries and explore the career path for Financial Analyst

Featured in The Course Notes

This course is mentioned in our blog, The Course Notes. Read one article that features Neural Networks and Deep Learning:
5 New Specializations that Teach You Future-Proof Skills
7 minute read

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.
Cover image
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.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Pattern Recognition and Machine Learning
Save
Offers a mathematical perspective on machine learning and will be useful for those seeking a deeper understanding of the underlying algorithms.
Pattern Recognition and Machine Learning...
Hardcover
$$$
Pattern Recognition and Machine Learning...
Paperback
$$$
Pattern Recognition and Machine Learning (text...
Unknown Binding
$$$$
Cover image
Statistical Foundations of Actuarial Learning and...
Save
Provides a gentle introduction to neural networks and deep learning, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students, researchers, and practitioners who want to learn more about neural networks and deep learning.
Statistical Foundations of Actuarial Learning and...
Paperback
$$
Cover image
Deep Learning
Save
Offers a hands-on guide to deep learning for practitioners.
Deep Learning: A Practitioner's Approach
Paperback
$$
(Français) Deep learning en action - Une approche par la...
Kindle Edition
$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Offers hands-on exercises that cover machine learning fundamentals and will aid in applying the concepts learned in this course.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Grokking Deep Learning
Save
Offers a practical and intuitive approach to understanding deep learning.
Grokking Deep Learning
Paperback
$$
Grokking Deep Learning
Kindle Edition
$$
Cover image
Probability and Statistics for Machine Learning
Save
Provides a broad overview of neural networks and deep learning.
Probability and Statistics for Machine Learning: A...
Hardcover
$$$
Cover image
Deep Learning with Python, Second Edition
Save
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.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
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.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Cross-Lingual Word Embeddings
Save
Provides a comprehensive overview of deep learning for natural language processing, covering the basics of deep learning, natural language processing, and applications of deep learning to natural language processing. It valuable resource for students, researchers, and practitioners who want to learn more about deep learning for natural language processing.
Cross-Lingual Word Embeddings (Synthesis Lectures...
Paperback
$$

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Course image
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 ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
This course belongs to a collection called Deep Learning. It's comprised of five courses:
  1. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
  2. Structuring Machine Learning Projects
  3. Convolutional Neural Networks
  4. Sequence Models
  5. Neural Networks and Deep Learning (viewing)
Traffic lights
Read about what's good
what should give you pause
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
Featured in The Course Notes
Read one article that features this course, Neural Networks and Deep Learning from our blog, The Course Notes:
5 New Specializations that Teach You Future-Proof Skills
7 minute read
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