Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- 4.7
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Approx. 30 hours to complete
Course Summary
Learn the basics of TensorFlow, one of the most popular open-source machine learning libraries, with this course. Discover how to build machine learning models and deep learning neural networks using TensorFlow.Key Learning Points
- Gain a strong foundation in machine learning and deep learning concepts
- Learn how to use TensorFlow to build and train models
- Explore real-world applications of machine learning and deep learning
Related Topics for further study
Learning Outcomes
- Develop a strong understanding of machine learning and deep learning concepts
- Learn how to build and train models using TensorFlow
- Gain practical experience with real-world applications of machine learning and deep learning
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Hands-on programming assignments
Similar Courses
- Applied Data Science with Python
- Deep Learning
- Machine Learning
Related Education Paths
Notable People in This Field
- Yann LeCun
- Francois Chollet
Related Books
Description
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.
Knowledge
- Learn best practices for using TensorFlow, a popular open-source machine learning framework
- Build a basic neural network in TensorFlow
- Train a neural network for a computer vision application
- Understand how to use convolutions to improve your neural network
Outline
- A New Programming Paradigm
- Introduction: A conversation with Andrew Ng
- A primer in machine learning
- The ‘Hello World’ of neural networks
- Working through ‘Hello World’ in TensorFlow and Python
- Before you begin: TensorFlow 2.0 and this course
- From rules to data
- Try it for yourself
- Have questions? Meet us on Discourse!
- Introduction to Google Colaboratory
- Week 1 Resources
- Introduction to Computer Vision
- A Conversation with Andrew Ng
- An Introduction to computer vision
- Writing code to load training data
- Coding a Computer Vision Neural Network
- Walk through a Notebook for computer vision
- Using Callbacks to control training
- Walk through a notebook with Callbacks
- Exploring how to use data
- The structure of Fashion MNIST data
- See how it's done
- Get hands-on with computer vision
- See how to implement Callbacks
- Week 2 Resources
- Enhancing Vision with Convolutional Neural Networks
- A conversation with Andrew Ng
- What are convolutions and pooling?
- Implementing convolutional layers
- Implementing pooling layers
- Improving the Fashion classifier with convolutions
- Walking through convolutions
- Coding convolutions and pooling layers
- Learn more about convolutions
- Getting hands-on, your first ConvNet
- Try it for yourself
- Experiment with filters and pools
- Week 3 Resources
- Using Real-world Images
- A conversation with Andrew Ng
- Understanding ImageGenerator
- Defining a ConvNet to use complex images
- Training the ConvNet with fit_generator
- Walking through developing a ConvNet
- Walking through training the ConvNet with fit_generator
- Adding automatic validation to test accuracy
- Exploring the impact of compressing images
- A conversation with Andrew
- Explore an impactful, real-world solution
- Designing the neural network
- Train the ConvNet with ImageGenerator
- Exploring the solution
- Training the neural network
- Experiment with the horse or human classifier
- Get hands-on and use validation
- Get Hands-on with compacted images
- Week 4 Resources
- Wrap up
Summary of User Reviews
Key Aspect Users Liked About This Course
The course has a great balance of theory and practical applications.Pros from User Reviews
- Well-structured course with clear explanations and examples.
- Great for beginners who want to get into machine learning and deep learning.
- Exercises and assignments are challenging but doable.
- Instructor is knowledgeable and engaging.
Cons from User Reviews
- Some users found the pace of the course too slow.
- A few users found the assignments to be too difficult.
- Some users felt that the course could have gone into more depth in certain areas.
- A few users experienced technical difficulties with the platform.
- The course is not suitable for those who are already familiar with TensorFlow.
Laurence Moroney
4.7 Raiting
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