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.
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Spécialisation TensorFlow in Practicedeeplearning.aiÀ propos de ce cours
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Résultats de carrière des étudiants
40%
ont commencé une nouvelle carrière après avoir terminé ce cours
43%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
12%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
Approx. 30 heures pour terminer
Anglais
Sous-titres : Anglais, Espagnol, Russe, Japonais
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
Résultats de carrière des étudiants
40%
ont commencé une nouvelle carrière après avoir terminé ce cours
43%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
12%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
Approx. 30 heures pour terminer
Anglais
Sous-titres : Anglais, Espagnol, Russe, Japonais
Offert par
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
Programme du cours : ce que vous apprendrez dans ce cours
6 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.
6 heures pour terminer
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
Before you begin: TensorFlow 2.0 and this course10 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
7 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!
7 heures pour terminer
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 vision1 h
See how to implement Callbacks10 min
Week 2 Resources10 min
1 exercice pour s'entraîner
Week 2 Quiz
8 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.
8 heures pour terminer
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 yourself1 h
Experiment with filters and pools1 h
Week 3 Resources10 min
1 exercice pour s'entraîner
Week 3 Quiz
9 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 heures pour terminer
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
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 classifier1 h
Get hands-on and use validation30 min
Get Hands-on with compacted images30 min
Week 4 Resources10 min
Wrap up10 min
1 exercice pour s'entraîner
Week 4 Quiz
Avis
Meilleurs avis pour INTRODUCTION TO TENSORFLOW FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING
par AS8 mars 2019
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?
par JJ27 avr. 2019
Very well organized. Good speakers. Content is comprehensive for a Introductory Course. A little more explanation on Validation versus Testing and on some of the evaluation functions would be helpful.
par RD13 août 2019
Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.
par MG26 mars 2020
this course was helpful for me to learn more about CNN. it starts from basic concepts and improve that slowly and step by step. i truthfully recommend how what to start working with neural networks.
À propos du Spécialisation TensorFlow in Practice
Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework.
In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry!
AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.
Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization.
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