DeepLearning.AI - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
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Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning at Coursera Overview
Duration | 4 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning at Coursera Highlights
- Earn a Certificate of completion from deeplearning.ai on successful course completion
- Instructor - Laurence Moroney, AI Advocate
- Financial aid available
- Apply your skills with hands-on projects
- Learn on your own schedule
- Course videos and readings
- Graded quizzes and assignments
- Shareable Certificate upon completion
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning at Coursera Course details
- Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
- TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. In this hands-on, four-course Professional Certificate program, you?ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you?ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate. Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization.
- 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.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning at Coursera Curriculum
Week 1 - A New Programming Paradigm - 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. You'll be working with code that works well across both TensorFlow 1.x and the TensorFlow 2.0 alpha. To get started, check out the first video, a conversation between Andrew and Laurence that sets the theme for what you'll study...
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
Week 2 - 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!
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
Week 3 - 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.
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
Week 4 - 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!
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
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