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.
ofrecido por
Programa Especializado - TensorFlow in Practicedeeplearning.aiAcerca de este Curso
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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 4 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
You should take the first 2 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano
Qué aprenderás
Build natural language processing systems using TensorFlow
Process text, including tokenization and representing sentences as vectors
Apply RNNs, GRUs, and LSTMs in TensorFlow
Train LSTMs on existing text to create original poetry and more
Habilidades que obtendrás
Natural Language ProcessingTokenizationMachine LearningTensorflowRNNs
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 4 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
You should take the first 2 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano
ofrecido por
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.
Programa - Qué aprenderás en este curso
3 horas para completar
Sentiment in text
The first step in understanding sentiment in text, and in particular when training a neural network to do so is the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks!
3 horas para completar
13 videos (Total 30 minutos), 4 lecturas, 3 cuestionarios
13 videos
Introduction1m
Word based encodings2m
Using APIs2m
Notebook for lesson 12m
Text to sequence3m
Looking more at the Tokenizer1m
Padding2m
Notebook for lesson 24m
Sarcasm, really?2m
Working with the Tokenizer1m
Notebook for lesson 33m
Week 1 Wrap up21s
4 lecturas
Check out the code!10m
Check out the code!10m
News headlines dataset for sarcasm detection10m
Check out the code!10m
1 ejercicio de práctica
Week 1 Quiz
4 horas para completar
Word Embeddings
Last week you saw how to use the Tokenizer to prepare your text to be used by a neural network by converting words into numeric tokens, and sequencing sentences from these tokens. This week you'll learn about Embeddings, where these tokens are mapped as vectors in a high dimension space. With Embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This will begin the process of training a neural network to udnerstand sentiment in text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labelled 'positive' or 'negative' and determining which words in a sentence drive those meanings.
4 horas para completar
14 videos (Total 39 minutos), 7 lecturas, 3 cuestionarios
14 videos
Introduction2m
The IMBD dataset1m
Looking into the details4m
How can we use vectors?2m
More into the details2m
Notebook for lesson 110m
Remember the sarcasm dataset?1m
Building a classifier for the sarcasm dataset1m
Let’s talk about the loss function1m
Pre-tokenized datasets43s
Diving into the code (part 1)1m
Diving into the code (part 2)2m
Notebook for lesson 35m
7 lecturas
IMDB reviews dataset10m
Check out the code!10m
Check out the code!10m
TensorFlow datasets10m
Subwords text encoder10m
Check out the code!10m
Week 2 Wrap up10m
1 ejercicio de práctica
Week 2 Quiz
3 horas para completar
Sequence models
In the last couple of weeks you looked first at Tokenizing words to get numeric values from them, and then using Embeddings to group words of similar meaning depending on how they were labelled. This gave you a good, but rough, sentiment analysis -- words such as 'fun' and 'entertaining' might show up in a positive movie review, and 'boring' and 'dull' might show up in a negative one. But sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'. This week you'll start digging into a variety of model formats that are used in training models to understand context in sequence!
3 horas para completar
10 videos (Total 16 minutos), 7 lecturas, 3 cuestionarios
10 videos
Introduction2m
LSTMs2m
Implementing LSTMs in code1m
Accuracy and loss1m
A word from Laurence35s
Looking into the code1m
Using a convolutional network1m
Going back to the IMDB dataset1m
Tips from Laurence37s
7 lecturas
Link to Andrew's sequence modeling course10m
More info on LSTMs10m
Check out the code!10m
Check out the code!10m
Check out the code!10m
Exploring different sequence models10m
Week 3 Wrap up10m
1 ejercicio de práctica
Week 3 Quiz
3 horas para completar
Sequence models and literature
Taking everything that you've learned in training a neural network based on NLP, we thought it might be a bit of fun to turn the tables away from classification and use your knowledge for prediction. Given a body of words, you could conceivably predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, this week you'll build a poetry generator. It's trained with the lyrics from traditional Irish songs, and can be used to produce beautiful-sounding verse of it's own!
3 horas para completar
14 videos (Total 27 minutos), 5 lecturas, 3 cuestionarios
14 videos
Introduction1m
Looking into the code57s
Training the data2m
More on training the data1m
Notebook for lesson 18m
Finding what the next word should be2m
Example1m
Predicting a word1m
Poetry!40s
Looking into the code1m
Laurence the poet!1m
Your next task1m
A conversation with Andrew Ng1m
5 lecturas
Check out the code!10m
link to Laurence's poetry10m
Check out the code!10m
Link to generating text using a character-based RNN10m
Wrap up10m
1 ejercicio de práctica
Week 4 Quiz
Revisiones
Principales revisiones sobre NATURAL LANGUAGE PROCESSING IN TENSORFLOW
por GS26 de ago. de 2019
Excellent. Isn't Laurence just great! Fantastically deep knowledge, easy learning style, very practical presentation. And funny! A pure joy, highly relevant and extremely useful of course. Thank you!
por AS21 de jul. de 2020
Great course for anyone interested in NLP! This course focuses on practical learning instead of overburdening students with theory. Would recommend this to every NLP beginner/enthusiast out there!!
por DW19 de nov. de 2019
These classes are excelling practical examples of how to use tensorflow for various problem types. My only objection is they are slightly light on the actual, behind the scenes, math and intuition.
por MR2 de may. de 2020
The course is great, but the assignments were not designed as well as the ones in the previous courses. I believe that a careful design of the assignments could significant improve the experience.
Acerca de Programa especializado: 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.
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.
¿Recibiré crédito universitario por completar el Curso?
Este Curso no otorga crédito universitario, pero algunas universidades pueden aceptar los Certificados del curso para obtener crédito. Consulta con tu institución para obtener más información. Los Títulos en línea y los Certificados Mastertrack™ de Coursera brindan la oportunidad de obtener créditos universitarios.
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