wtorek, 17. grudzień 2019, Codete, Developing Bots. Primers - Workshop Kraków!
Z 17. grudzień 2019 - 9:00
Do 18. grudzień 2019 - 16:00
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Opis wydarzenia
Developing Bots. A Primer
During the workshop, we implement three different types of bots. We have prepared a few notebooks to be done before the course to the participants a short introduction into chatbots. We implement an HR assistant bot that can be a basis for your projects. We show how to develop Slack, Telegram and Messenger bots.
At the beginning of day on one of the courses we present bot taxonomy and machine learning terms related to bots. Next, we go through natural language processing and natural language understanding. Between both, a short introduction to tensorflow is given. It is needed for better understanding of how natural language understanding methods work.
During day two we go into more complex topics like sentiment analysis, context management and how to build intelligent bots. We show how to develop a vectorizer and build a sentiment analysis method. This method is next compared with other solutions that are available on the market. The context management is a complex topic and we show how to use some methods to deal with context recognition and management in case we have more than one. The last part is dedicated to generative models and how to build intelligent bots.
The course ends with a homework where the methods explained during the course should be used by the participants in given homework examples. Additionally, there is one optional notebook for speech recognition.
Prerequisites
Basic Python knowledge,
Recommended: basic machine learning knowledge
Pokaż więcej
During the workshop, we implement three different types of bots. We have prepared a few notebooks to be done before the course to the participants a short introduction into chatbots. We implement an HR assistant bot that can be a basis for your projects. We show how to develop Slack, Telegram and Messenger bots.
At the beginning of day on one of the courses we present bot taxonomy and machine learning terms related to bots. Next, we go through natural language processing and natural language understanding. Between both, a short introduction to tensorflow is given. It is needed for better understanding of how natural language understanding methods work.
During day two we go into more complex topics like sentiment analysis, context management and how to build intelligent bots. We show how to develop a vectorizer and build a sentiment analysis method. This method is next compared with other solutions that are available on the market. The context management is a complex topic and we show how to use some methods to deal with context recognition and management in case we have more than one. The last part is dedicated to generative models and how to build intelligent bots.
The course ends with a homework where the methods explained during the course should be used by the participants in given homework examples. Additionally, there is one optional notebook for speech recognition.
Prerequisites
Basic Python knowledge,
Recommended: basic machine learning knowledge
Pokaż więcej
Outcomes
Participants will understand…
how to use NLP methods,
how machine learning methods are used for bots,
generative and relative approaches to NLU,
how to use sentiment analysis.
Participants will be able to…
develop their own bot using common tools like NLTK, spaCy, Cortana and Alexa,
build machine learning models using common solutions in Python,
Agenda
BEFORE
Anna HR Slack bot
Slack and trello API introduction
Few examples how to deal with RTM and Events API
Setup Trello and use it to build an HR process pipeline
Greg stock market messenger bot
Messenger setup explanation
API explanation
Combine it with Alpha Vantage stock market API
Paul customer service telegram bot
Telegram API explanation
Building a simple Telegram bot
DAY 1
Introduction to bots:
Bot taxonomy
List of known bots and bot platforms
Usage examples
Machine learning and bots
Short explanation of NLP/NLU and machine learning usage for bots
Three generations of bots explanation
Natural Language Processing
Regular expressions and Python methods used for text processing
Corporas, NLTK and tokenization
Part of Speech and Tagging - examples with NLTK and Spacy
Text normalization
Lemmatization
Sentence extraction
Noun chunks
Named Entity Resolution
TFIDF and bag of words
Short introduction to Tensorflow
Tensorflow elements explanation
Build a linear regression model
Build a random forest classifier
Different types of neural network architectures
Build a recurrent neural network
Natural Language Understanding
Similarity measures
Vector Space Model explained
Type of vectorizers
Build a vectorizer with Tensorflow
Intent and entities in NLU explained
Using SpaCy language model and Rasa for intent understanding
DAY 2
Sentiment Analysis
Introduction into sentiment analysis
CoreNLP and TextRazor used for sentiment analysis
Implement a PCA Tfidf vectorizer for sentiment analysis
Build a simple sentiment analysis model
Context management
Introduction to the problem of context management
Use similarity measure to get the context of the conversation
Intents in context management
Implementation of a neural network for context recognition
Updating a model in tensorflow for continuous context learning
Intelligent bots
Different approaches to text generation
Text generation with a simple recurrent neural network
Introduction to generative models
Build a VGAN for answer generation
HOMEWORK
Anna - HR assistant bot
Build a dataset for Rasa to understand the intents to cover the full HR process pipeline
Use sentiment analysis to get the sentiment of candidate and company feedbacks
Greg - stock market advisor bot
Extract the nouns and users’ intent to manage the stock market actions
Paul - customer support bot
Use text generation models to response for customers’ questions
Introduce a neural network to get the context and build a management tool for it
Timeline
2-day workshops
We provide a delicious complimentary meal, coffee, and snacks.
If you have any further questions please contact us via
Participants will understand…
how to use NLP methods,
how machine learning methods are used for bots,
generative and relative approaches to NLU,
how to use sentiment analysis.
Participants will be able to…
develop their own bot using common tools like NLTK, spaCy, Cortana and Alexa,
build machine learning models using common solutions in Python,
Agenda
BEFORE
Anna HR Slack bot
Slack and trello API introduction
Few examples how to deal with RTM and Events API
Setup Trello and use it to build an HR process pipeline
Greg stock market messenger bot
Messenger setup explanation
API explanation
Combine it with Alpha Vantage stock market API
Paul customer service telegram bot
Telegram API explanation
Building a simple Telegram bot
DAY 1
Introduction to bots:
Bot taxonomy
List of known bots and bot platforms
Usage examples
Machine learning and bots
Short explanation of NLP/NLU and machine learning usage for bots
Three generations of bots explanation
Natural Language Processing
Regular expressions and Python methods used for text processing
Corporas, NLTK and tokenization
Part of Speech and Tagging - examples with NLTK and Spacy
Text normalization
Lemmatization
Sentence extraction
Noun chunks
Named Entity Resolution
TFIDF and bag of words
Short introduction to Tensorflow
Tensorflow elements explanation
Build a linear regression model
Build a random forest classifier
Different types of neural network architectures
Build a recurrent neural network
Natural Language Understanding
Similarity measures
Vector Space Model explained
Type of vectorizers
Build a vectorizer with Tensorflow
Intent and entities in NLU explained
Using SpaCy language model and Rasa for intent understanding
DAY 2
Sentiment Analysis
Introduction into sentiment analysis
CoreNLP and TextRazor used for sentiment analysis
Implement a PCA Tfidf vectorizer for sentiment analysis
Build a simple sentiment analysis model
Context management
Introduction to the problem of context management
Use similarity measure to get the context of the conversation
Intents in context management
Implementation of a neural network for context recognition
Updating a model in tensorflow for continuous context learning
Intelligent bots
Different approaches to text generation
Text generation with a simple recurrent neural network
Introduction to generative models
Build a VGAN for answer generation
HOMEWORK
Anna - HR assistant bot
Build a dataset for Rasa to understand the intents to cover the full HR process pipeline
Use sentiment analysis to get the sentiment of candidate and company feedbacks
Greg - stock market advisor bot
Extract the nouns and users’ intent to manage the stock market actions
Paul - customer support bot
Use text generation models to response for customers’ questions
Introduce a neural network to get the context and build a management tool for it
Timeline
2-day workshops
We provide a delicious complimentary meal, coffee, and snacks.
If you have any further questions please contact us via
Developing Bots. Primers - Workshop Kraków!, Codete zdarzenie
źródło: Eventbrite
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nie jest gospodarzem tego wydarzenia! Organizatorzy tutaj kontaktowe.
