Crash course in chat bots
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A brief overview of chat bots: artificial intelligence and machine learning in the context of natural language processing, prediction and fulfillment. I used https://dialogflow.com/ and Google Cloud Functions for the demo.
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Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
NATURAL LANGUAGE PROCESSING.pptx
This document discusses natural language processing (NLP), including its definition, applications, how to build an NLP pipeline, phases of NLP, challenges of NLP, and advantages and disadvantages. NLP involves using machines to understand, analyze, manipulate and interpret human language. It has applications in areas like question answering, machine translation, sentiment analysis, spelling correction and chatbots. Building an NLP pipeline typically involves steps like tokenization, lemmatization, parsing and named entity recognition. NLP faces challenges from ambiguities in language.
Introduction to Natural Language Processing
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. In this blog, we'll explore the basics of NLP and its techniques, from text classification to sentiment analysis. We'll explain how NLP works and why it's become such an important tool for businesses and organizations in recent years. We'll also delve into some of the most popular NLP tools and libraries, such as NLTK and spaCy, and provide examples of how they can be used to analyze and process text data. Whether you're a seasoned data scientist or just starting out in the world of NLP, this blog has something for everyone. So come along and discover the power of natural language processing!
Natural language processing using python
Natural language processing (NLP) is concerned with interactions between computers and human languages. NLP analyzes text to handle tasks like summarization, translation, sentiment analysis, and topic segmentation. The Natural Language Toolkit (NLTK) is a Python library that provides tools for NLP tasks like tokenization, stemming, tagging, parsing, and classification. Tokenization is the process of splitting text into tokens or chunks. Bag-of-words is an algorithm that encodes text as numeric vectors, representing word presence or absence to allow machine learning on text data. NLP has applications in areas like spam filtering, chatbots, and sentiment analysis.
Pycon India 2018 Natural Language Processing Workshop
1. The document discusses an introduction to natural language processing (NLP) including definitions of key NLP concepts and techniques.
2. It provides examples of common NLP tasks like sentiment analysis, entity recognition, and gender prediction and shows code for performing these tasks.
3. The document concludes with an overview of the Google Cloud Natural Language API for applying NLP techniques through a REST API.
Generative AI and the Rise of Large Language Models
A slideshow tackling a hot new topic: GenAI. Visually explains the core fundemental mechanisms behind LLMs and Generative Models.
Deep learning for NLP and Transformer
This document provides an overview of deep learning basics for natural language processing (NLP). It discusses the differences between classical machine learning and deep learning, and describes several deep learning models commonly used in NLP, including neural networks, recurrent neural networks (RNNs), encoder-decoder models, and attention models. It also provides examples of how these models can be applied to tasks like machine translation, where two RNNs are jointly trained on parallel text corpora in different languages to learn a translation model.
Natural Language Processing
This document provides an overview of natural language processing (NLP) and the use of deep learning for NLP tasks. It discusses how deep learning models can learn representations and patterns from large amounts of unlabeled text data. Deep learning approaches are now achieving superior results to traditional NLP methods on many tasks, such as named entity recognition, machine translation, and question answering. However, deep learning models do not explicitly model linguistic knowledge. The document outlines common NLP tasks and how deep learning algorithms like LSTMs, CNNs, and encoder-decoder models are applied to problems involving text classification, sequence labeling, and language generation.
Deep learning seminar report
This document provides an overview of deep learning including definitions, architectures, types of deep learning networks, and applications. It defines deep learning as a branch of machine learning that uses neural networks with multiple hidden layers to perform feature extraction and transformation without being explicitly programmed. The main architectures discussed are deep neural networks, deep belief networks, and recurrent neural networks. The types of deep learning networks covered include feedforward neural networks, recurrent neural networks, convolutional neural networks, restricted Boltzmann machines, and autoencoders. Finally, the document discusses several applications of deep learning across industries such as self-driving cars, natural language processing, virtual assistants, and healthcare.
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Crash course in chat bots
- 1. Crash Course in Chat Bots
- 2. Terminology AI is machine intelligence AI makes use of artificial neural networks (ANN) to process information ML is the teaching of the ANN DL is teaching an ANN with multiple hidden layers
- 3. WhatisAI? AI is turning data into rules through statistical analysis. The rules are imprinted into the neural network through learning. It can be used for anything. It’s not perfect (but neither is hard-coding of rules) It can evolve.
- 4. What happens when I talk to AI?
- 5. 1:Transcription (Nuance Transcription Engine training material is available)
- 9. Transcription uses deep learning to convert speech to text
- 10. 2.PartsofSpeech Tagging The biggest problem for POS is the lack of a bad grammar training set
- 12. NLP/NLU= POS tagging+NER+ NED Named Entity Recognition Named Entity Disambiguation AI uses context and not rules AI infers meaning from statistical probability AI answers have associated probabilities
- 13. What does this mean? Example
- 14. Quiz Define “Kallax” (Without Googling!)
- 15. Answer NNP
- 16. Addsome Context “I’m flying out of Kallax.”
- 17. VastlyImproved Answer Kallax is an airport.
- 19. Intent Recognition Process and understand the input to extract the user’s intention Alexa Bixby Cortana Google Nuance
- 20. Fulfilment Does the work of finding the answer Usually a web service Alibaba Cloud AWS Azure Google Cloud On Premise
- 21. Let’s do it! I’m going to use Google tools to demonstrate
Editor's Notes
- In the past it used energy patterns and required a lot of per user training. That is no longer needed.
- Not neuro-linguistic programming! The biggest problem here is the lack of any bad grammar training set.
- Anyone have good trivial knowledge?
- You have never encountered this word before (hopefully).
- Reasoning: It looks like a proper noun because it is capitalised.
- Now, once you read “of” you have already defined the highest probability for the word following “of”.