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 Specialization 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.
The full deeplearning.ai TensorFlow Specialization will be available later this year, but you can get started with Course 1, Introduction to Tensorflow for AI, ML and DL, available now on Coursera.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
proposé par
deeplearning.ai
À propos de ce cours
4.7
163 notes
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Computer VisionTensorflowMachine Learning
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
13 heures pour terminer
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...
4 vidéos (Total 16 min), 5 lectures, 3 quiz
4 vidéos
A primer in machine learning3 min
The ‘Hello World’ of neural networks5 min
Working through ‘Hello World’ in TensorFlow and Python3 min
5 lectures
Learner Support10 min
From rules to data10 min
Try it for yourself10 min
Introduction to Google Colaboratory10 min
Week 1 Resources10 min
1 exercice pour s'entraîner
Week 1 Quiz
Semaine
24 heures pour terminer
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!
7 vidéos (Total 15 min), 6 lectures, 3 quiz
7 vidéos
An Introduction to computer vision2 min
Writing code to load training data2 min
Coding a Computer Vision Neural Network2 min
Walk through a Notebook for computer vision3 min
Using Callbacks to control training1 min
Walk through a notebook with Callbacks1 min
6 lectures
Exploring how to use data10 min
The structure of Fashion MNIST data10 min
See how it's done10 min
Get hands-on with computer visions
See how to implement Callbacks10 min
Week 2 Resources10 min
1 exercice pour s'entraîner
Week 2 Quiz
Semaine
35 heures pour terminer
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.
6 vidéos (Total 19 min), 6 lectures, 3 quiz
6 vidéos
What are convolutions and pooling?2 min
Implementing convolutional layers1 min
Implementing pooling layers4 min
Improving the Fashion classifier with convolutions4 min
Walking through convolutions3 min
6 lectures
Coding convolutions and pooling layers10 min
Learn more about convolutions10 min
Getting hands-on, your first ConvNet10 min
Try it for yourselfs
Experiment with filters and poolss
Week 3 Resources10 min
1 exercice pour s'entraîner
Week 3 Quiz
Semaine
46 heures pour terminer
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!
9 vidéos (Total 27 min), 10 lectures, 3 quiz
9 vidéos
Understanding ImageGenerator4 min
Defining a ConvNet to use complex images2 min
Training the ConvNet with fit_generator2 min
Walking through developing a ConvNet2 min
Walking through training the ConvNet with fit_generator3 min
Adding automatic validation to test accuracy4 min
Exploring the impact of compressing images3 min
Outro: A conversation with Andrew1 min
10 lectures
Explore an impactful, real-world solution10 min
Designing the neural network10 min
Train the ConvNet with ImageGenerator10 min
Exploring the solution10 min
Training the neural network10 min
Experiment with the horse or human classifiers
Get hands-on and use validation30 min
Get Hands-on with compacted images30 min
Week 4 Resources10 min
Outro10 min
1 exercice pour s'entraîner
Week 4 Quiz
4.7
32 avisMeilleurs avis
Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?
without a doubt, Laurence's teaching is much better than reading the documentation! The course is a great starting point!
Enseignant
Laurence Moroney
AI AdvocateGoogle Brain
À propos de deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
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