Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
Ce cours fait partie de la Spécialisation TensorFlow: Data and Deployment
Browser-based Models with TensorFlow.js
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À propos de ce cours
83,345 consultations récentes
Ce que vous allez apprendre
Train and run inference in a browser
Handle data in a browser
Build an object classification and recognition model using a webcam
Compétences que vous acquerrez
Convolutional Neural NetworkMachine LearningTensorflowObject DetectionTensorFlow.js
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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
Basic understanding of JavaScript
Approx. 13 heures pour terminer
Recommandé : 4 weeks of study, 4-5 hours/week
Anglais
Sous-titres : Anglais
Programme du cours : ce que vous apprendrez dans ce cours
5 heures pour terminer
Introduction to TensorFlow.js
Welcome to Browser-based Models with TensorFlow.js, the first course of the TensorFlow for Data and Deployment Specialization. In this first course, we’re going to look at how to train machine learning models in the browser and how to use them to perform inference using JavaScript. This will allow you to use machine learning directly in the browser as well as on backend servers like Node.js. In the first week of the course, we are going to build some basic models using JavaScript and we'll execute them in simple web pages.
5 heures pour terminer
11 vidéos (Total 30 min), 7 lectures, 3 quiz
11 vidéos
Course Introduction, A Conversation with Andrew Ng1 min
A Few Words From Laurence2 min
Building the Model3 min
Training the Model3 min
First Example In Code4 min
The Iris Dataset1 min
Reading the Data4 min
One-hot Encoding1 min
Designing the NN2 min
Iris Classifier In Code6 min
7 lectures
Getting Your System Ready10 min
Downloading the Coding Examples and Exercises10 min
Your First Model10 min
Iris Dataset Documentation10 min
Using the Web Server10 min
Iris Classifier10 min
Week 1 Wrap up10 min
2 exercices pour s'entraîner
Quiz 1
One-Hot Encoding
4 heures pour terminer
Image Classification In the Browser
This week we'll look at Computer Vision problems, including some of the unique considerations when using JavaScript, such as handling thousands of images for training. By the end of this module you will know how to build a site that lets you draw in the browser and recognizes your handwritten digits!
4 heures pour terminer
8 vidéos (Total 27 min), 5 lectures, 2 quiz
8 vidéos
Creating a Convolutional Net with JavaScript4 min
Visualizing the Training Process2 min
What Is a Sprite Sheet?1 min
Using the Sprite Sheet2 min
Using tf.tidy() to Save Memory1 min
A Few Words From Laurence24s
MNIST Classifier In Code13 min
5 lectures
tjs-vis Documentation10 min
MNIST Sprite Sheet10 min
MNIST Classifier10 min
Week 2 Wrap up10 min
Exercise Description10 min
1 exercice pour s'entraîner
Week 2 Quiz
5 heures pour terminer
Converting Models to JSON Format
This week we'll see how to take models that have been created with TensorFlow in Python and convert them to JSON format so that they can run in the browser using Javascript. We will start by looking at two models that have already been pre-converted. One of them is going to be a toxicity classifier, which uses NLP to determine if a phrase is toxic in a number of categories; the other one is Mobilenet which can be used to detect content in images. By the end of this module, you will train a model in Python yourself and convert it to JSON format using the tensorflow.js converter.
5 heures pour terminer
12 vidéos (Total 28 min), 7 lectures, 2 quiz
12 vidéos
A Few Words From Laurence1 min
Pre-trained TensorFlow.js Models49s
Toxicity Classifier3 min
Toxicity Classifier In Code3 min
MobileNet49s
Using MobileNet1 min
Training Results1 min
MobileNet Example In Code3 min
Converting Models to JavaScript4 min
Converting Models to JavaScript In Code2 min
Linear Example In Code1 min
7 lectures
Important Links10 min
Toxicity Classifier10 min
Classes Supported by MobileNet10 min
Image Classification Using MobileNet10 min
Linear Model10 min
Week 3 Wrap up10 min
Optional - Install Wget (Only If Needed)10 min
1 exercice pour s'entraîner
Week 3 Quiz
4 heures pour terminer
Transfer Learning with Pre-Trained Models
One final work type that you'll need when creating Machine Learned applications in the browser is to understand how transfer learning works. This week you'll build a complete web site that uses TensorFlow.js, capturing data from the web cam, and re-training mobilenet to recognize Rock, Paper and Scissors gestures.
4 heures pour terminer
11 vidéos (Total 26 min), 3 lectures, 2 quiz
11 vidéos
A Few Words From Laurence53s
Building a Simple Web Page2 min
Retraining the MobileNet Model1 min
The Training Function2 min
Capturing the Data3 min
The Dataset Class2 min
Training the Network with the Captured Data1 min
Performing Inference4 min
Rock Paper Scissors In Code4 min
A Conversation with Andrew Ng1 min
3 lectures
Rock Paper Scissors10 min
Exercise Description10 min
Wrap up10 min
1 exercice pour s'entraîner
Week 4 Quiz
Avis
4.7
Meilleurs avis pour BROWSER-BASED MODELS WITH TENSORFLOW.JS
par AZMar 28, 2020
No doubt, the team of Deeplearning.AI is building best learning resources. We would love to get more and more resources for easy way learning from DeepLearning.AI Team.\n\nThanks to all.
par ZBDec 19, 2019
Awesome - elegant in its complex simplicity. Clear explanations, logical curriculum structure, nice and knowledgeable code examples. A must-complete course indeed!
par SSJan 1, 2020
Thanks to Laurence and Andrew for designing such a great course. I learnt a lot from this course and looking forward to learn more from both of you.
par SZFeb 23, 2020
Awesome course! This is one of the most practical courses I have taken, and I am looking forward to the next courses in the series. Thanks! - Steve
par JCMar 2, 2020
I really enjoy working on the programming assignments of this course especially the Week 4 one which is fun and have a lot to learn!
par VVDec 17, 2019
Great course! A bit of a learning curve on javascript, but really worth it! The exercise in Week 4 was phenomenal! Thanks.
par PMDec 17, 2019
Last exercise was really difficult to pass! Probably due to overfitting and not generalising to the the test set.
par PGDec 16, 2019
Great videos and nice coding challenges. Would like some more support on various error messages though
par ECJan 4, 2020
Excellent! well presented and guided through. A good starting point to begin playing with the tech!
par RSFeb 19, 2020
Great resource to build practical, real-world examples...easily explained with lots of sample code.
par MBDec 27, 2019
Great introduction on browser-based models. Well presented with great code examples and exercises.
par OHFeb 6, 2020
This was an awesome course with everything well explained and with some really cool projects.
par SBFeb 5, 2020
Good introductory course on Tensorflow deployment. Enjoyed lectures and doing assignments.
par PTMar 7, 2020
Great for a beginner. Projects you can show in interviews can be made using given skills
par SAJan 3, 2020
Great course to begin with for those who wants to upgrade their deployment methods
par HAFeb 27, 2020
Thank you for the great course, I have learned a lot in a short time!
par RRJan 13, 2020
I've learned a lot of concepts and do a lot of excises, thanks a lok!
par DJJan 5, 2020
The course is really clear, but I feel that it's lacking in content.
par VSDec 31, 2019
Awesome course with very good content.Thanks Laurence.
par USFeb 26, 2020
Amazing content and a great way of explaining stuff
À propos de deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
À propos du Spécialisation TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models.
In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more.
Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever.
This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment.
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