Este curso forma parte de Programa especializado: TensorFlow: Data and Deployment

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827 calificaciones

Laurence Moroney

31.252 ya inscrito

ofrecido por

Programa especializado: TensorFlow: Data and Deploymentdeeplearning.ai

Acerca de este Curso

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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.
In this first course, you’ll train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.

This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Fechas límite flexibles

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Certificado para compartir

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100 % en línea

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Curso 1 de 4 en

Programa Especializado - TensorFlow: Data and Deployment

Nivel intermedio

Basic understanding of JavaScript

Aprox. 18 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)

Qué aprenderás

Train and run inference in a browser

Handle data in a browser

Build an object classification and recognition model using a webcam

Habilidades que obtendrás

Convolutional Neural NetworkMachine LearningTensorflowObject DetectionTensorFlow.js

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 1 de 4 en

Programa Especializado - TensorFlow: Data and Deployment

Nivel intermedio

Basic understanding of JavaScript

Aprox. 18 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)

Instructor

Calificación del instructor

4,76/5 (151 calificaciones)

Laurence Moroney

Instructor

Lead AI Advocate, Google

323.908 alumnos

15 cursos

ofrecido por

deeplearning.ai

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Programa - Qué aprenderás en este curso

Calificación del contenido95%(2,323 calificaciones)

Semana

Semana 1

6 horas para completar

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.

6 horas para completar

11 videos (Total 30 minutos), 7 lecturas, 4 cuestionarios

11 videos

Specialization Introduction, A Conversation with Andrew Ng59s

Course Introduction, A Conversation with Andrew Ng1m

A Few Words From Laurence2m

Building the Model3m

Training the Model3m

First Example In Code4m

The Iris Dataset1m

Reading the Data4m

One-hot Encoding1m

Designing the NN2m

Iris Classifier In Code6m

7 lecturas

Getting Your System Ready10m

Downloading the Ungraded Labs and Programming Assignments10m

Your First Model10m

Iris Dataset Documentation10m

Using the Web Server10m

Iris Classifier10m

Week 1 Wrap up10m

2 ejercicios de práctica

Quiz 1

One-Hot Encoding

Semana

Semana 2

4 horas para completar

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 horas para completar

8 videos (Total 27 minutos), 5 lecturas, 2 cuestionarios

8 videos

Introduction, A Conversation with Andrew Ng1m

Creating a Convolutional Net with JavaScript4m

Visualizing the Training Process2m

What Is a Sprite Sheet?1m

Using the Sprite Sheet2m

Using tf.tidy() to Save Memory1m

A Few Words From Laurence24s

MNIST Classifier In Code13m

5 lecturas

tfjs-vis Documentation10m

MNIST Sprite Sheet10m

MNIST Classifier10m

Week 2 Wrap up10m

Exercise Description10m

1 ejercicio de práctica

Week 2 Quiz

Semana

Semana 3

4 horas para completar

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.

4 horas para completar

12 videos (Total 28 minutos), 6 lecturas, 2 cuestionarios

12 videos

Introduction, A Conversation with Andrew Ng1m

A Few Words From Laurence1m

Pre-trained TensorFlow.js Models49s

Toxicity Classifier3m

Toxicity Classifier In Code3m

MobileNet49s

Using MobileNet1m

Training Results1m

MobileNet Example In Code3m

Converting Models to JavaScript4m

Converting Models to JavaScript In Code2m

Linear Example In Code1m

6 lecturas

Important Links10m

Toxicity Classifier10m

Classes Supported by MobileNet10m

Image Classification Using MobileNet10m

Linear Model10m

Week 3 Wrap up10m

1 ejercicio de práctica

Week 3 Quiz

Semana

Semana 4

4 horas para completar

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 horas para completar

11 videos (Total 26 minutos), 3 lecturas, 2 cuestionarios

11 videos

Introduction, A Conversation with Andrew Ng1m

A Few Words From Laurence53s

Building a Simple Web Page2m

Retraining the MobileNet Model1m

The Training Function2m

Capturing the Data3m

The Dataset Class2m

Training the Network with the Captured Data1m

Performing Inference4m

Rock Paper Scissors In Code4m

A Conversation with Andrew Ng1m

3 lecturas

Rock Paper Scissors10m

Exercise Description10m

Wrap up10m

1 ejercicio de práctica

Week 4 Quiz

Reseñas

4.7

186 reseña

5 stars
80,69 %
4 stars
14,95 %
3 stars
2,65 %
2 stars
0,72 %
1 star
0,96 %

Principales reseñas sobre BROWSER-BASED MODELS WITH TENSORFLOW.JS

por EJ17 de mar. de 2021

This course is very practical and interesting. I enjoyed the excitement I got along the way.\n\nIt was modeled to make you pass as long as you want to pass.\n\nThank you Laurence and Andrew.

por AZ28 de mar. de 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. Thanks to all.

por TB24 de dic. de 2020

I have worked with tensorflow for some time, but I didn't know it is this straight forward to deploy on browser.Very good explanation with examples of different deployment options

por SA7 de jul. de 2020

course contents are good and explained very well with one problem of audio, audio is not clear and pitch is low but I like this course. as a beginner, this course is best.

Ver todas las reseñas

Acerca de Programa especializado: 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. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Cuál es la política de reembolsos?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Hay ayuda económica disponible?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.

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Browser-based Models with TensorFlow.js