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Laurence Moroney
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
Read more
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
Read more
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/rjgnxe/introduction
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What's inside
Syllabus
A New Programming Paradigm
Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In Week 1, you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along. To get started, check out the first video, a conversation between Andrew and Laurence that sets the theme for what you'll study...
Read more
Syllabus
A New Programming Paradigm
Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In Week 1, you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along. To get started, check out the first video, a conversation between Andrew and Laurence that sets the theme for what you'll study...
Read more
Introduction to Computer Vision
Welcome to week 2 of the course! In week 1 you learned all about how Machine Learning and Deep Learning is a new programming paradigm. This week you’re going to take that to the next level by beginning to solve problems of computer vision with just a few lines of code!
Check out this conversation between Laurence and Andrew where they discuss it and introduce you to Computer Vision!
Enhancing Vision with Convolutional Neural Networks
Welcome to week 3! In week 2 you saw a basic Neural Network for Computer Vision. It did the job nicely, but it was a little naive in its approach. This week we’ll see how to make it better, as discussed by Laurence and Andrew here.
Using Real-world Images
Last week you saw how to improve the results from your deep neural network using convolutions. It was a good start, but the data you used was very basic. What happens when your images are larger, or if the features aren’t always in the same place? Andrew and Laurence discuss this to prepare you for what you’ll learn this week: handling complex images!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces basic and advanced concepts of Tensorflow for scalable AI-powered algorithms
Taught by Laurence Moroney, who is widely recognized for his work in TensorFlow and AI
Emphasizes a new programming paradigm for solving computer vision problems
Involves building and applying scalable models to real-world problems
May not cover all the foundational principles of Machine Learning and Deep Learning
Save this course
Save Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning to your list so you can find it easily later:
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Reviews summary
Introduction to tensorflow
learners say this is a great introduction to Tensorflow, especially for beginners. Students who have taken Andrew Ng's Deep Learning Specialization may find it too easy. The course starts with implementing a simple neural network and then progresses onto convolutional neural networks. It ends with a very useful data management topic on training data labelling. One of the major benefits of this course is that you can start learning by doing right from week 1. The instructor doesn't spend time explaining every line of code with every detail. That leaves you to figure out a few things on your own due to which you're bound to remember these details.
The instructor has a clear and to the point, with some good exercises.
"The instructor has a clear and to the point, with some good exercises."
This course is coming for me after the Machine Learning course from Andrew Ng and it gives very hands-on answer to theorical part deep dived. Tensorflow is really easy to jump in, and this course give a perfect overview of the potentiel.
"This course is coming for me after the Machine Learning course from Andrew Ng and it gives very hands-on answer to theorical part deep dived."
"Tensorflow is really easy to jump in, and this course give a perfect overview of the potentiel."
The course is well paced, the assignments balanced, and overall provided a lot of experience in learning real-world usage of neural networks.
"The course is well paced, the assignments balanced, and overall provided a lot of experience in learning real-world usage of neural networks."
The course is heavily focused on hands-on application using Python. The course will encourage you to experiment and understand the impact of different parameters in your model. It's fun and challenging.
"The course will encourage you to experiment and understand the impact of different parameters in your model."
"It's fun and challenging."
Each week you will have like 2 minute video explaining the NN that you will use.
"Each week you will have like 2 minute video explaining the NN that you will use."
This course is really about Keras and not tensorflow.
"This course is really about Keras and not tensorflow."
In comparison to the deep learning specialization assignments, the ones we had in these course are really poor in terms of information and clarity on what we have to do.
"In comparison to the deep learning specialization assignments, the ones we had in these course are really poor in terms of information and clarity on what we have to do."
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 Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning with these
activities:
TensorFlow Cheat Sheet
Show steps
Stay organized and improve recall by compiling a cheat sheet of key TensorFlow concepts and commands.
Browse courses on
TensorFlow
Show steps
-
Gather relevant TensorFlow information from the course materials.
-
Condense the information into a concise and easy-to-reference format.
Data Visualization
Show steps
Strengthen your data visualization skills to effectively present your TensorFlow results.
Browse courses on
Data Visualization
Show steps
-
Review basic data visualization techniques.
-
Learn about different data visualization libraries, such as Matplotlib or Seaborn.
Review Calculus and Linear Algebra
Show steps
Refresh your understanding of Calculus and Linear Algebra to strengthen your foundation for ML concepts.
Browse courses on
Calculus
Show steps
-
Review key concepts from Calculus, such as derivatives and integrals.
-
Review key concepts from Linear Algebra, such as matrices and vectors.
Six other activities
Expand to see all activities and additional details
Show all nine activities
TensorFlow Tutorial
Show steps
Enhance your understanding of TensorFlow by following guided tutorials that demonstrate its practical applications.
Browse courses on
TensorFlow
Show steps
-
Follow a TensorFlow tutorial to build a simple machine learning model.
-
Experiment with different TensorFlow APIs to explore their capabilities.
TensorFlow Study Group
Show steps
Enhance your learning and resolve challenges by collaborating with peers in a TensorFlow study group.
Browse courses on
TensorFlow
Show steps
-
Find or form a study group with other students taking the course.
-
Meet regularly to discuss course materials, work on projects, and share knowledge.
TensorFlow Exercises
Show steps
Reinforce your understanding of TensorFlow by solving coding exercises and practice problems.
Browse courses on
TensorFlow
Show steps
-
Solve coding problems on platforms like LeetCode or HackerRank.
-
Participate in TensorFlow-focused coding challenges.
Grokking Deep Learning
Show steps
Expand your understanding of deep learning concepts through Andrew Trask's comprehensive and intuitive book.
View
Grokking Deep Learning
on Amazon
Show steps
-
Read the book to gain a deeper understanding of deep learning principles.
-
Work through the practice exercises to reinforce your knowledge.
TensorFlow Blog Post
Show steps
Deepen your understanding of TensorFlow by writing a blog post that explains a specific concept or application.
Browse courses on
TensorFlow
Show steps
-
Choose a specific TensorFlow topic or application to focus on.
-
Research and gather information on the topic.
-
Write a comprehensive blog post that explains the topic in a clear and concise manner.
-
Publish your blog post on a relevant platform.
TensorFlow Competition
Show steps
Challenge yourself and put your TensorFlow skills to the test by participating in a competition.
Browse courses on
TensorFlow
Show steps
-
Identify and register for a TensorFlow-focused competition.
-
Build a machine learning model using TensorFlow.
-
Submit your model to the competition.
-
Analyze the results and identify areas for improvement.
TensorFlow Cheat Sheet
Show steps
Stay organized and improve recall by compiling a cheat sheet of key TensorFlow concepts and commands.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Gather relevant TensorFlow information from the course materials.
- Condense the information into a concise and easy-to-reference format.
Data Visualization
Show steps
Strengthen your data visualization skills to effectively present your TensorFlow results.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Review basic data visualization techniques.
- Learn about different data visualization libraries, such as Matplotlib or Seaborn.
Review Calculus and Linear Algebra
Show steps
Refresh your understanding of Calculus and Linear Algebra to strengthen your foundation for ML concepts.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review key concepts from Calculus, such as derivatives and integrals.
- Review key concepts from Linear Algebra, such as matrices and vectors.
Six other activities
Expand to see all activities and additional details
Show all nine activities
TensorFlow Tutorial
Show steps
Enhance your understanding of TensorFlow by following guided tutorials that demonstrate its practical applications.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Follow a TensorFlow tutorial to build a simple machine learning model.
- Experiment with different TensorFlow APIs to explore their capabilities.
TensorFlow Study Group
Show steps
Enhance your learning and resolve challenges by collaborating with peers in a TensorFlow study group.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find or form a study group with other students taking the course.
- Meet regularly to discuss course materials, work on projects, and share knowledge.
TensorFlow Exercises
Show steps
Reinforce your understanding of TensorFlow by solving coding exercises and practice problems.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Solve coding problems on platforms like LeetCode or HackerRank.
- Participate in TensorFlow-focused coding challenges.
Grokking Deep Learning
Show steps
Expand your understanding of deep learning concepts through Andrew Trask's comprehensive and intuitive book.
View
Grokking Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book to gain a deeper understanding of deep learning principles.
- Work through the practice exercises to reinforce your knowledge.
TensorFlow Blog Post
Show steps
Deepen your understanding of TensorFlow by writing a blog post that explains a specific concept or application.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Choose a specific TensorFlow topic or application to focus on.
- Research and gather information on the topic.
- Write a comprehensive blog post that explains the topic in a clear and concise manner.
- Publish your blog post on a relevant platform.
TensorFlow Competition
Show steps
Challenge yourself and put your TensorFlow skills to the test by participating in a competition.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify and register for a TensorFlow-focused competition.
- Build a machine learning model using TensorFlow.
- Submit your model to the competition.
- Analyze the results and identify areas for improvement.
Career center
Learners who complete Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists use TensorFlow to analyze data and build machine learning models. This course will teach you how to use TensorFlow to perform data analysis and build machine learning models, which can help you to become a successful data scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers use TensorFlow to build and implement machine learning models. This course will teach you how to use TensorFlow to develop and deploy machine learning models, which can help you to advance your career as a machine learning engineer.
Software Engineer
Software Engineer
TensorFlow is a popular open-source framework for machine learning, and it is used by many software engineers to build scalable AI-powered algorithms. This course will teach you best practices for using TensorFlow, which can help you to develop the skills needed to become a successful software engineer in the field of artificial intelligence and machine learning.
Speech Recognition Engineer
Speech Recognition Engineer
Speech recognition engineers use TensorFlow to build and implement speech recognition systems. This course will teach you how to use TensorFlow to develop and deploy speech recognition systems, which can help you to advance your career as a speech recognition engineer.
Computer Vision Engineer
Computer Vision Engineer
Computer vision engineers use TensorFlow to build and implement computer vision systems. This course will teach you how to use TensorFlow to develop and deploy computer vision systems, which can help you to advance your career as a computer vision engineer.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial intelligence engineers use TensorFlow to build and implement artificial intelligence systems. This course will teach you how to use TensorFlow to develop and deploy artificial intelligence systems, which can help you to advance your career as an artificial intelligence engineer.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural language processing engineers use TensorFlow to build and implement natural language processing systems. This course will teach you how to use TensorFlow to develop and deploy natural language processing systems, which can help you to advance your career as a natural language processing engineer.
Deep Learning Engineer
Deep Learning Engineer
Deep learning engineers use TensorFlow to build and implement deep learning models. This course will teach you how to use TensorFlow to develop and deploy deep learning models, which can help you to advance your career as a deep learning engineer.
Machine Learning Researcher
Machine Learning Researcher
Machine learning researchers use TensorFlow to develop new machine learning algorithms and techniques. This course will teach you how to use TensorFlow to conduct machine learning research, which can help you to advance your career as a machine learning researcher.
Deep Learning Researcher
Deep Learning Researcher
Deep learning researchers use TensorFlow to develop new deep learning algorithms and techniques. This course will teach you how to use TensorFlow to conduct deep learning research, which can help you to advance your career as a deep learning researcher.
Computer Vision Researcher
Computer Vision Researcher
Computer vision researchers use TensorFlow to develop new computer vision algorithms and techniques. This course will teach you how to use TensorFlow to conduct computer vision research, which can help you to advance your career as a computer vision researcher.
Natural Language Processing Researcher
Natural Language Processing Researcher
Natural language processing researchers use TensorFlow to develop new natural language processing algorithms and techniques. This course will teach you how to use TensorFlow to conduct natural language processing research, which can help you to advance your career as a natural language processing researcher.
Speech Recognition Researcher
Speech Recognition Researcher
Speech recognition researchers use TensorFlow to develop new speech recognition algorithms and techniques. This course will teach you how to use TensorFlow to conduct speech recognition research, which can help you to advance your career as a speech recognition researcher.
Artificial Intelligence Professor
Artificial Intelligence Professor
Artificial intelligence professors use TensorFlow to teach artificial intelligence and machine learning courses. This course will teach you how to use TensorFlow to develop and deliver artificial intelligence and machine learning courses, which can help you to advance your career as an artificial intelligence professor.
Machine Learning Professor
Machine Learning Professor
Machine learning professors use TensorFlow to teach machine learning courses. This course will teach you how to use TensorFlow to develop and deliver machine learning courses, which can help you to advance your career as a machine learning professor.
For more career information including salaries, visit:
OpenCourser.com/course/rjgnxe/introduction
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
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning.
Provides a hands-on approach to machine learning using TensorFlow and Scikit-Learn. Its comprehensive coverage of machine learning concepts and practical examples will supplement your understanding gained in this course.
Focuses on the practical aspects of deep learning using Python and TensorFlow. It offers a clear and beginner-friendly introduction to the fundamentals of deep learning, providing a valuable resource for your learning journey.
Provides a comprehensive overview of artificial intelligence concepts and techniques. Its coverage of machine learning, deep learning, and natural language processing will broaden your knowledge and support your understanding of the course material.
Comprehensive guide to deep learning using TensorFlow 2 and Keras. Its detailed explanations and practical examples will enhance your understanding of deep learning architectures and their implementation.
Offers a thorough introduction to machine learning using Python. Its comprehensive coverage of machine learning algorithms and techniques will provide you with a strong foundation for your studies.
Introduces deep learning using Fastai and PyTorch. Its practical approach and code-centric examples will provide you with additional insights into deep learning implementation.
Provides a detailed introduction to machine learning using R. While it does not focus specifically on TensorFlow, it will provide you with a strong foundation in machine learning concepts and techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rjgnxe/introduction
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Effort
4 weeks, 4-5 hours/week
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Laurence Moroney
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
DeepLearning.AI TensorFlow Developer. It's comprised of four courses:
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (viewing)
- Convolutional Neural Networks in TensorFlow
- Sequences, Time Series and Prediction
- Natural Language Processing in TensorFlow
Good to know
Introduces basic and advanced concepts of Tensorflow for scalable AI-powered algorithms
Taught by Laurence Moroney, who is widely recognized for his work in TensorFlow and AI
Emphasizes a new programming paradigm for solving computer vision problems
Involves building and applying scalable models to real-world problems
May not cover all the foundational principles of Machine Learning and Deep Learning
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