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.
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課程信息
You should take the first 2 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
您將學到的內容有
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
您將獲得的技能
- Natural Language Processing
- Tokenization
- Machine Learning
- Tensorflow
- RNNs
You should take the first 2 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
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deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授課大綱 - 您將從這門課程中學到什麼
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!
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 understand 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.
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!
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!
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來自NATURAL LANGUAGE PROCESSING IN TENSORFLOW的熱門評論
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!
Good course that gives you basic understanding of word embeddings, sequence analysis, and many other things. Thank you for Mr. Moroney and the entire Coursera team for making it available.
Excellent course Gave me a brief idea with practical experience about how to process strings for machine learning.\n\nI would like to thank Laurence Sir\n\nand a Special thanks to Andrew Sir
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.
關於 DeepLearning.AI TensorFlow Developer 專業證書
TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.

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