Natural Language Processing (NLP) is a field of artificial intelligence that deals with interactions between computers and human languages. NLP aims to program computers to process and analyze large amounts of natural language data. Some common NLP tasks include speech recognition, text classification, machine translation, question answering, and more. Popular NLP tools include Stanford CoreNLP, NLTK, OpenNLP, and TextBlob. Vectorization is commonly used to represent text in a way that can be used for machine learning algorithms like calculating text similarity. Tf-idf is a common technique used to weigh words based on their frequency and importance.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
This document provides an outline on natural language processing and machine vision. It begins with an introduction to different levels of natural language analysis, including phonetic, syntactic, semantic, and pragmatic analysis. Phonetic analysis constructs words from phonemes using frequency spectrograms. Syntactic analysis builds a structural description of sentences through parsing. Semantic analysis generates a partial meaning representation from syntax, while pragmatic analysis uses context. The document also introduces machine vision as a technology using optical sensors and cameras for industrial quality control through detection of faults. It operates through sensing images, processing/analyzing images, and various applications.
Natural Language Processing (NLP) is a subfield of artificial intelligence that aims to help computers understand human language. NLP involves analyzing text at different levels, including morphology, syntax, semantics, discourse, and pragmatics. The goal is to map language to meaning by breaking down sentences into syntactic structures and assigning semantic representations based on context. Key steps include part-of-speech tagging, parsing sentences into trees, resolving references between sentences, and determining intended meaning and appropriate actions. Together, these allow computers to interpret and respond to natural human language.
The document discusses natural language and natural language processing (NLP). It defines natural language as languages used for everyday communication like English, Japanese, and Swahili. NLP is concerned with enabling computers to understand and interpret natural languages. The summary explains that NLP involves morphological, syntactic, semantic, and pragmatic analysis of text to extract meaning and understand context. The goal of NLP is to allow humans to communicate with computers using their own language.
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.
This document provides an overview of Word2Vec, a neural network model for learning word embeddings developed by researchers led by Tomas Mikolov at Google in 2013. It describes the goal of reconstructing word contexts, different word embedding techniques like one-hot vectors, and the two main Word2Vec models - Continuous Bag of Words (CBOW) and Skip-Gram. These models map words to vectors in a neural network and are trained to predict words from contexts or predict contexts from words. The document also discusses Word2Vec parameters, implementations, and other applications that build upon its approach to word embeddings.
This document discusses neural network models for natural language processing tasks like machine translation. It describes how recurrent neural networks (RNNs) were used initially but had limitations in capturing long-term dependencies and parallelization. The encoder-decoder framework addressed some issues but still lost context. Attention mechanisms allowed focusing on relevant parts of the input and using all encoded states. Transformers replaced RNNs entirely with self-attention and encoder-decoder attention, allowing parallelization while generating a richer representation capturing word relationships. This revolutionized NLP tasks like machine translation.
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
The document provides an overview of the Natural Language Toolkit (NLTK). It discusses that NLTK is a Python library for natural language processing that includes corpora, tokenizers, stemmers, part-of-speech taggers, parsers, and other tools. The document outlines the modules in NLTK and their functionality, such as the nltk.corpus module for corpora, nltk.tokenize and nltk.stem for tokenizers and stemmers, and nltk.tag for part-of-speech tagging. It also provides instructions on installing NLTK and downloading its data.
Word embedding, Vector space model, language modelling, Neural language model, Word2Vec, GloVe, Fasttext, ELMo, BERT, distilBER, roBERTa, sBERT, Transformer, Attention
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Introduction to natural language processingMinh Pham
This document provides an introduction to natural language processing (NLP). It discusses what NLP is, why NLP is a difficult problem, the history of NLP, fundamental NLP tasks like word segmentation, part-of-speech tagging, syntactic analysis and semantic analysis, and applications of NLP like information retrieval, question answering, text summarization and machine translation. The document aims to give readers an overview of the key concepts and challenges in the field of natural language processing.
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
Natural language processing and transformer modelsDing Li
The document discusses several approaches for text classification using machine learning algorithms:
1. Count the frequency of individual words in tweets and sum for each tweet to create feature vectors for classification models like regression. However, this loses some word context information.
2. Use Bayes' rule and calculate word probabilities conditioned on class to perform naive Bayes classification. Laplacian smoothing is used to handle zero probabilities.
3. Incorporate word n-grams and context by calculating word probabilities within n-gram contexts rather than independently. This captures more linguistic information than the first two approaches.
Natural language processing (NLP) is introduced, including its definition, common steps like morphological analysis and syntactic analysis, and applications like information extraction and machine translation. Statistical NLP aims to perform statistical inference for NLP tasks. Real-world applications of NLP are discussed, such as automatic summarization, information retrieval, question answering and speech recognition. A demo of a free NLP application is presented at the end.
Natural Language Processing (NLP) - IntroductionAritra Mukherjee
This presentation provides a beginner-friendly introduction towards Natural Language Processing in a way that arouses interest in the field. I have made the effort to include as many easy to understand examples as possible.
In this presentation presented in AI & ML meetup on 2nd Feb, Sangram Mishra develops the same NLP solution using NLTK and OpenNLP, Sangram compares and contrasts the two open source technologies for deeper understanding and insights on choosing and using them for real-world projects.
This document provides an overview of the OpenNLP natural language processing tool. It discusses the various NLP tasks that OpenNLP can perform, including tokenization, POS tagging, named entity recognition, chunking, parsing, and co-reference resolution. It also describes how models for these tasks are trained in OpenNLP using annotated training data. The document concludes by listing some advantages and limitations of OpenNLP.
Designing A Project Using Java ProgrammingKaty Allen
The document discusses the Connector class in the NS simulator. The Connector class is a subclass of NsObject and is the superclass for basic network components that handle packets, such as hubs and links. Connector objects have a single output queue, while Classifier objects can have multiple output queues. When packets traverse connections in the NS simulator, various follow objects are embedded in the connections to log events such as enqueue, dequeue, drop, and receive to trace packet flow.
This document provides an industrial training presentation on Python programming. It introduces Python, explaining that it is an interpreted, object-oriented, high-level programming language. It then covers why Python is used, its basic data types like numbers, strings, lists, dictionaries, tuples and sets. The presentation also discusses Python concepts like conditionals, loops, functions, exceptions, file handling, object-oriented programming and databases. It concludes that Python supports both procedural and object-oriented programming and can be universally accepted. References for further reading are also included.
This document provides an introduction to natural language processing (NLP) and the Natural Language Toolkit (NLTK) module for Python. It discusses how NLP aims to develop systems that can understand human language at a deep level, lists common NLP applications, and explains why NLP is difficult due to language ambiguity and complexity. It then describes how corpus-based statistical approaches are used in NLTK to tackle NLP problems by extracting features from text corpora and using statistical models. The document gives an overview of the main NLTK modules and interfaces for common NLP tasks like tagging, parsing, and classification. It provides an example of word tokenization and discusses tokens and types in NLTK.
This document provides an introduction to the Python programming language. It is divided into four sections: Getting Started, Data Types, Control Flow, and Additional Exercises. The Getting Started section covers downloading Python, writing "Hello World" programs, and performing basic arithmetic operations. The Data Types section explains strings, numbers, booleans, variables, and arrays. The Control Flow section previews selection, iteration, and procedures. The Additional Exercises section provides straightforward, challenging, and file handling exercises for readers.
This document summarizes text classification in PHP. It discusses what text classification is, common natural language processing terminology like tokenization and stemming, Bayes' theorem and how it relates to naive Bayes classification. It provides examples of tokenizing, stemming, stopping words, and building a naive Bayes classifier in PHP using the NlpTools library. Key steps like training and testing a classifier on sample text data are demonstrated.
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool Kit). Shankar had previously presented a comprehensive overview of the NLTK last December at the Python meetup. The Python NLTK is a very powerful collection of libraries that can be applied to a variety of NLP applications such as sentiment analysis. His presentation from last December may be found here (click on Boston Python Meetup Materials) : http://www.shankarambady.com/
The document discusses natural language processing techniques for analyzing blogs and other documents. It covers topics like sentence boundary detection, part-of-speech tagging, and text summarization algorithms. Specifically, it mentions Luhn's summarization algorithm which scores sentences based on the frequency of important words squared divided by the total number of words in the sentence.
The Ring programming language version 1.7 book - Part 43 of 196Mahmoud Samir Fayed
The document discusses using the Ring language to develop web applications through a CGI library. It describes how to configure the Apache web server to support Ring CGI scripts by enabling certain options and handlers. It also provides an overview of the CGI library and how to define commands that can be executed through CGI scripts to interface with the web server and return output.
NLTK is a popular Python library for natural language processing. It provides tools for tasks like tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, parsing, and language models. NLTK includes functions, classes, and sample datasets to support research and development in NLP. It is open source, easy to use, well documented, and supports many common NLP tasks and algorithms.
Try to imagine the amount of time and effort it would take you to write a bug-free script or application that will accept a URL, port scan it, and for each HTTP service that it finds, it will create a new thread and perform a black box penetration testing while impersonating a Blackberry 9900 smartphone. While you’re thinking, Here’s how you would have done it in Hackersh:
“http://localhost” \
-> url \
-> nmap \
-> browse(ua=”Mozilla/5.0 (BlackBerry; U; BlackBerry 9900; en) AppleWebKit/534.11+ (KHTML, like Gecko) Version/7.1.0.346 Mobile Safari/534.11+”) \
-> w3af
Meet Hackersh (“Hacker Shell”) – A new, free and open source cross-platform shell (command interpreter) with built-in security commands and Pythonect-like syntax.
Aside from being interactive, Hackersh is also scriptable with Pythonect. Pythonect is a new, free, and open source general-purpose dataflow programming language based on Python, written in Python. Hackersh is inspired by Unix pipeline, but takes it a step forward by including built-in features like remote invocation and threads. This 120 minute lab session will introduce Hackersh, the automation gap it fills, and its features. Lots of demonstrations and scripts are included to showcase concepts and ideas.
The document outlines the topics covered in a 5-day Certified Python Programmer For Data Science course. Day 1 covers an introduction to programming and Python basics. Day 2 covers Jupyter Notebook, functions, modules, object-oriented programming. Day 3 covers working with files, JSON data, and web scraping. Day 4 introduces NumPy, Pandas, and Matplotlib for data analysis and visualization. Day 5 covers machine learning and a capstone project.
This document is a summer training report submitted by Shubham Yadav to the Department of Information Technology at Rajkiya Engineering College. The report details Shubham's 4-week training program at IQRA Software Technologies where he learned about Python programming language and its libraries like NumPy, Matplotlib, Pandas, and OpenCV. The report includes sections on the history of Python, its characteristics, data structures in Python, file handling, and how to use various Python libraries for tasks like mathematical operations, data visualization, data analysis, and computer vision.
This document provides an overview of several popular Python libraries for natural language processing (NLP): NLTK, Pattern, TextBlob, and Texthero. It describes some of the key NLP tasks each library can perform, like tokenization, lemmatization, stemming, sentiment analysis, spellchecking, and data mining. NLTK is presented as a toolkit for statistical NLP with functionalities for many NLP tasks. Pattern is useful for text processing and data mining from sources like Twitter and Google. TextBlob builds on NLTK and Pattern, and makes NLP easy to use with operations like noun phrase extraction and sentiment analysis. Texthero focuses on text analysis and visualization to efficiently work with textual data.
The document discusses several key points about Python:
1. It summarizes praise for Python from programmers and companies like Google, NASA, and CCP Games, highlighting Python's simplicity, compactness, and ability to quickly develop applications.
2. It introduces common Python concepts like strings, lists, sequences, namespaces, polymorphism, and duck typing. Strings can be manipulated using slicing and methods. Lists and other sequences support indexing, slicing, and iteration.
3. Python uses name-based rather than type-based polymorphism through duck typing - an object's capabilities are defined by its methods and properties rather than its class.
- Python is an interpreted, object-oriented programming language that is beginner friendly and open source. It was created in the 1990s and named after Monty Python.
- Python is very suitable for natural language processing tasks due to its built-in string and list datatypes as well as libraries like NLTK. It also has strong numeric processing capabilities useful for machine learning.
- Python code is organized using functions, classes, modules, and packages to improve structure. It is interpreted at runtime rather than requiring a separate compilation step.
This document discusses several popular Python libraries:
- NumPy is a fundamental package for scientific computing and machine learning that represents data as n-dimensional arrays. Its array interface allows representing images, sounds, and other data as arrays.
- Pandas allows working with and analyzing datasets, including functions for cleaning, exploring, and manipulating data. It can analyze big data and draw conclusions.
- Pyttsx3 is a text-to-speech library that can convert text to speech offline, unlike some other libraries.
- Wikipedia allows programmatically accessing and parsing data from Wikipedia, including searching, getting article summaries and linked data.
- Other standard Python modules discussed include datetime for date/time handling, webbrowser for controlling browsers,
This was a brief 1-hour introduction to R programming, presented at the 1st Inter-experimental Machine Learning (IML) Working Group Workshop at CERN, 20-22 March 2017.
Understanding computer vision with Deep LearningCloudxLab
Computer vision is a branch of computer science which deals with recognising objects, people and identifying patterns in visuals. It is basically analogous to the vision of an animal.
Topics covered:
1. Overview of Machine Learning
2. Basics of Deep Learning
3. What is computer vision and its use-cases?
4. Various algorithms used in Computer Vision (mostly CNN)
5. Live hands-on demo of either Auto Cameraman or Face recognition system
6. What next?
This document provides an agenda for an introduction to deep learning presentation. It begins with an introduction to basic AI, machine learning, and deep learning terms. It then briefly discusses use cases of deep learning. The document outlines how to approach a deep learning problem, including which tools and algorithms to use. It concludes with a question and answer section.
This document discusses recurrent neural networks (RNNs) and their applications. It begins by explaining that RNNs can process input sequences of arbitrary lengths, unlike other neural networks. It then provides examples of RNN applications, such as predicting time series data, autonomous driving, natural language processing, and music generation. The document goes on to describe the fundamental concepts of RNNs, including recurrent neurons, memory cells, and different types of RNN architectures for processing input/output sequences. It concludes by demonstrating how to implement basic RNNs using TensorFlow's static_rnn function.
- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets.
- It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted.
- It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.
An autoencoder is an artificial neural network that is trained to copy its input to its output. It consists of an encoder that compresses the input into a lower-dimensional latent-space encoding, and a decoder that reconstructs the output from this encoding. Autoencoders are useful for dimensionality reduction, feature learning, and generative modeling. When constrained by limiting the latent space or adding noise, autoencoders are forced to learn efficient representations of the input data. For example, a linear autoencoder trained with mean squared error performs principal component analysis.
The document discusses challenges in training deep neural networks and solutions to those challenges. Training deep neural networks with many layers and parameters can be slow and prone to overfitting. A key challenge is the vanishing gradient problem, where the gradients shrink exponentially small as they propagate through many layers, making earlier layers very slow to train. Solutions include using initialization techniques like He initialization and activation functions like ReLU and leaky ReLU that do not saturate, preventing gradients from vanishing. Later improvements include the ELU activation function.
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/5u2RiS )
This CloudxLab Reinforcement Learning tutorial helps you to understand Reinforcement Learning in detail. Below are the topics covered in this tutorial:
1) What is Reinforcement?
2) Reinforcement Learning an Introduction
3) Reinforcement Learning Example
4) Learning to Optimize Rewards
5) Policy Search - Brute Force Approach, Genetic Algorithms and Optimization Techniques
6) OpenAI Gym
7) The Credit Assignment Problem
8) Inverse Reinforcement Learning
9) Playing Atari with Deep Reinforcement Learning
10) Policy Gradients
11) Markov Decision Processes
Apache Spark - Key Value RDD - Transformations | Big Data Hadoop Spark Tutori...CloudxLab
The document provides information about key-value RDD transformations and actions in Spark. It defines transformations like keys(), values(), groupByKey(), combineByKey(), sortByKey(), subtractByKey(), join(), leftOuterJoin(), rightOuterJoin(), and cogroup(). It also defines actions like countByKey() and lookup() that can be performed on pair RDDs. Examples are given showing how to use these transformations and actions to manipulate key-value RDDs.
Advanced Spark Programming - Part 2 | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kyRTuW
This CloudxLab Advanced Spark Programming tutorial helps you to understand Advanced Spark Programming in detail. Below are the topics covered in this slide:
1) Shared Variables - Accumulators & Broadcast Variables
2) Accumulators and Fault Tolerance
3) Custom Accumulators - Version 1.x & Version 2.x
4) Examples of Broadcast Variables
5) Key Performance Considerations - Level of Parallelism
6) Serialization Format - Kryo
7) Memory Management
8) Hardware Provisioning
Apache Spark - Dataframes & Spark SQL - Part 2 | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sm9c61
This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide:
1) Loading XML
2) What is RPC - Remote Process Call
3) Loading AVRO
4) Data Sources - Parquet
5) Creating DataFrames From Hive Table
6) Setting up Distributed SQL Engine
Apache Spark - Dataframes & Spark SQL - Part 1 | Big Data Hadoop Spark Tutori...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sf2z6i
This CloudxLab Introduction to Spark SQL & DataFrames tutorial helps you to understand Spark SQL & DataFrames in detail. Below are the topics covered in this slide:
1) Introduction to DataFrames
2) Creating DataFrames from JSON
3) DataFrame Operations
4) Running SQL Queries Programmatically
5) Datasets
6) Inferring the Schema Using Reflection
7) Programmatically Specifying the Schema
Apache Spark - Running on a Cluster | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
(Big Data with Hadoop & Spark Training: http://bit.ly/2IUsWca
This CloudxLab Running in a Cluster tutorial helps you to understand running Spark in the cluster in detail. Below are the topics covered in this tutorial:
1) Spark Runtime Architecture
2) Driver Node
3) Scheduling Tasks on Executors
4) Understanding the Architecture
5) Cluster Managers
6) Executors
7) Launching a Program using spark-submit
8) Local Mode & Cluster-Mode
9) Installing Standalone Cluster
10) Cluster Mode - YARN
11) Launching a Program on YARN
12) Cluster Mode - Mesos and AWS EC2
13) Deployment Modes - Client and Cluster
14) Which Cluster Manager to Use?
15) Common flags for spark-submit
Introduction to SparkR | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2LCTufA
This CloudxLab Introduction to SparkR tutorial helps you to understand SparkR in detail. Below are the topics covered in this tutorial:
1) SparkR (R on Spark)
2) SparkR DataFrames
3) Launch SparkR
4) Creating DataFrames from Local DataFrames
5) DataFrame Operation
6) Creating DataFrames - From JSON
7) Running SQL Queries from SparkR
Introduction to NoSQL | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
1) NoSQL databases are non-relational and schema-free, providing alternatives to SQL databases for big data and high availability applications.
2) Common NoSQL database models include key-value stores, column-oriented databases, document databases, and graph databases.
3) The CAP theorem states that a distributed data store can only provide two out of three guarantees around consistency, availability, and partition tolerance.
Introduction to MapReduce - Hadoop Streaming | Big Data Hadoop Spark Tutorial...CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sh5b3E
This CloudxLab Hadoop Streaming tutorial helps you to understand Hadoop Streaming in detail. Below are the topics covered in this tutorial:
1) Hadoop Streaming and Why Do We Need it?
2) Writing Streaming Jobs
3) Testing Streaming jobs and Hands-on on CloudxLab
Introduction To TensorFlow | Deep Learning Using TensorFlow | CloudxLabCloudxLab
This document provides instructions for getting started with TensorFlow using a free CloudxLab. It outlines the following steps:
1. Open CloudxLab and enroll if not already enrolled. Otherwise go to "My Lab".
2. In "My Lab", open Jupyter and run commands to clone an ML repository containing TensorFlow examples.
3. Go to the deep learning folder in Jupyter and open the TensorFlow notebook to get started with examples.
Introduction to Deep Learning | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/goQxnL )
This CloudxLab Deep Learning tutorial helps you to understand Deep Learning in detail. Below are the topics covered in this tutorial:
1) What is Deep Learning
2) Deep Learning Applications
3) Artificial Neural Network
4) Deep Learning Neural Networks
5) Deep Learning Frameworks
6) AI vs Machine Learning
In this tutorial, we will learn the the following topics -
+ The Curse of Dimensionality
+ Main Approaches for Dimensionality Reduction
+ PCA - Principal Component Analysis
+ Kernel PCA
+ LLE
+ Other Dimensionality Reduction Techniques
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
In this tutorial, we will learn the the following topics -
+ Training and Visualizing a Decision Tree
+ Making Predictions
+ Estimating Class Probabilities
+ The CART Training Algorithm
+ Computational Complexity
+ Gini Impurity or Entropy?
+ Regularization Hyperparameters
+ Regression
+ Instability
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
UiPath Community Day Kraków: Devs4Devs ConferenceUiPathCommunity
We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
November 18, 2021
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 :
- Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants.
- REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.
2. Natural Language Processing
Natural Language Processing
● Natural-language processing (NLP) is an area of computer science and
artificial intelligence concerned with the interactions between
computers and human languages
● In particular how to program computers to fruitfully process large
amounts of natural language data
3. Natural Language Processing
Natural Language Processing
In 1950, Alan Turing published an article titled "Computing Machinery and
Intelligence" which proposed what is now called the Turing test as a
criterion of intelligence.
5. Natural Language Processing
Natural Language Processing
Knowledge Base – It contains the database
of information that is used to equip chatbots
with the information needed to respond to
queries of customers request.
Data Store – It contains interaction history
of chatbot with users.
6. Natural Language Processing
Natural Language Processing
NLP Layer – It translates
users queries (free form)
into information that can
be used for appropriate
responses.
Application Layer – It is
the application interface
that is used to interact with
the user
7. Natural Language Processing
Natural Language Processing - Applications
● Speech Recognition - The task of speech recognition is to map an
acoustic signal containing a spoken natural language utterance into the
corresponding sequence of words intended by the speaker.
8. Natural Language Processing
Natural Language Processing - Applications
● Text Classification - Given an example of text, predict a predefined
class label
9. Natural Language Processing
Natural Language Processing - Applications
● Caption Generation - It is the problem of describing the contents of
an image.
10. Natural Language Processing
● Machine Translation - Machine translation is the problem of
converting a source text in one language to another language.
Natural Language Processing - Applications
11. Natural Language Processing
Natural Language Processing - Applications
● Question Answering - It is the problem where given a
subject, such as a document of text, answer a specific question
about the subject.
12. Natural Language Processing
The most popular Natural Language Processing Tools are:
● Stanford's Core NLP Suite
● Natural Language Toolkit
● Apache Lucene and Solr
● Apache OpenNLP
● Text Blob which is a wrapper over the NLTK library
Natural Language Processing - Tools
13. Natural Language Processing
Natural Language Processing - Tools
Let us use the TextBlob library of Python to Build a program that
makes a quiz out of a provided text.
It is basically a usage of NER - Named-entity recognition
14. Natural Language Processing
Natural Language Processing - TextBlob
Let us begin by importing TextBlob and then selecting a text.
>>> from textblob import TextBlob
Now you can either load a text from a file as
>>> f = open(‘filename.txt’)
>>> text = f.read()
Or assign the text to a variable as
>>> text = “World War II (often abbreviated to
WWII or WW2), also known as the Second World War,
was a ….. “
15. Natural Language Processing
Natural Language Processing - TextBlob
Next we’ll convert our text to a TextBlob object.
>>> text = TextBlob(text)
Now we are ready to apply different methods on our text.
16. Natural Language Processing
Natural Language Processing - TextBlob
Let us understand a few things about the TextBlob api -
text.sentences - gives the sentences in a text
sentences.tags - gives the tags for each of the word in sentence.
It returns a list of tuples with the word being the first element of
the tuple and the tag being the second.
17. Natural Language Processing
Natural Language Processing - TextBlob
Now to generate our quiz
● We will extract each sentence
● We will replace all the nouns and proper nouns
with a blank from each sentences.
● To make it easy we will remove only after the
fourth word in the sentence.
18. Natural Language Processing
Natural Language Processing - TextBlob
>>> ww2b = TextBlob(ww2)
>>> for sentence in ww2b.sentences:
new_sentence = sentence
for index, tag in enumerate(sentence.tags):
if tag[1] in ('NN', 'NNP') and index > 3:
new_sentence =
new_sentence.replace(tag[0], "____")
print(new_sentence)
print("n==================n")
Run it on Notebook
20. Natural Language Processing
Natural Language Processing - Tools
We are given the task of finding the most related posts from a bunch
of posts.
The tricky thing that we have to tackle first is how to turn
text into something on which we can calculate similarity ??
21. Natural Language Processing
Natural Language Processing - Tools
How to do it ??
Bag of Words Approach -
It totally ignores the order of words and simply uses word counts as
their basis.
In this model, a text, such as a sentence or a document is represented
as the bag (multiset) of its words, disregarding grammar and even
word order but keeping multiplicity.
22. Natural Language Processing
Natural Language Processing - Tools
Vectorization
● For each word in the post, its occurrence is counted and noted in a
vector.
● This step is also called vectorization.
● The vector is typically huge as it contains as many elements as
words occur in the whole dataset.
23. Natural Language Processing
Natural Language Processing - Tools
Vectorization - Example
For the two statements - "How to format my hard disk" and " Hard
disk format problems " the vectors are shown below
aka Term Document Matrix
24. Natural Language Processing
Natural Language Processing - Tools
Vectorization - Using Scikit learn
>>> from sklearn.feature_extraction.text import
CountVectorizer
>>> vectorizer = CountVectorizer(min_df=1)
The min_df parameter determines how CountVectorizer treats seldom words
● If it is set to an integer, all words occurring less than that value will be
dropped
● If it is a fraction, all words that occur in less than that fraction of the overall
dataset will be dropped.
25. Natural Language Processing
Natural Language Processing - Tools
Vectorization - Using Scikit learn
>>> content = ["How to format my hard disk", " Hard
disk format problems "]
>>> X = vectorizer.fit_transform(content)
>>> vectorizer.get_feature_names()
['disk', 'format', 'hard', 'how', 'my', 'problems',
'to']
Run it on Notebook
26. Natural Language Processing
Natural Language Processing - Tools
Vectorization - Using Scikit learn
>>> print(X.toarray().transpose())
[[1 1]
[1 1]
[1 1]
[1 0]
[1 0]
[0 1]
[1 0]]
This means that the first sentence contains all the
words except "problems",
while the second contains all but "how", "my", and
"to".
27. Natural Language Processing
Natural Language Processing - Tools
Finding Distance
We can measure distance between two vectors using the Euclidean
Distance.
But first we will normalize each vectors.
The scipy.linalg module provides a function called norm.
The norm() function calculates the Euclidean norm (shortest
distance)
29. Natural Language Processing
Natural Language Processing - Tools
Applying Everything we learnt on a toy dataset
Now we will consider 5 toy posts and find the similarity with a given
post.
>>> post1 = "This is a toy post about machine learning.
Actually, it contains not much interesting stuff."
>>> post2 = "Imaging databases can get huge."
>>> post3 = "Most imaging databases save images
permanently."
>>> post4 = "Imaging databases store images."
>>>post5 = "Imaging databases store images. Imaging
databases store images. Imaging databases store images."
30. Natural Language Processing
Natural Language Processing - Tools
Applying Everything we learnt on a toy dataset
Now we will build our vectorizer
>>> posts = [post1, post2, post3, post4, post5]
>>> X_train = vectorizer.fit_transform(posts)
>>> num_samples, num_features = X_train.shape
>>> print("#samples: %d, #features: %d" %
(num_samples, num_features))
#samples: 5, #features: 24
As we provided 5 different posts and there are 24 different words
in them.
31. Natural Language Processing
Natural Language Processing - Tools
Applying Everything we learnt on a toy dataset
Finally we will iterate through all the vectors of
the posts and find their distance with the new post.
Perform on Notebook
33. Natural Language Processing
Natural Language Processing
In this section we will see how to:
● Load the file contents and the categories
● Extract feature vectors suitable for machine learning
● Train a linear model to perform categorization
● Use a grid search strategy to find a good configuration of both the
feature extraction components and the classifier
34. Natural Language Processing
Natural Language Processing
Loading the 20 newsgroups dataset
To load the dataset use the code-
>>> categories = ['alt.atheism',
'soc.religion.christian', 'comp.graphics', 'sci.med']
>>> from sklearn.datasets import fetch_20newsgroups
>>> twenty_train = fetch_20newsgroups(subset='train',
categories=categories, shuffle=True, random_state=42)
35. Natural Language Processing
Natural Language Processing
Loading the 20 newsgroups dataset
To load the dataset use the code-
>>> categories = ['alt.atheism',
'soc.religion.christian', 'comp.graphics', 'sci.med']
>>> from sklearn.datasets import fetch_20newsgroups
>>> twenty_train = fetch_20newsgroups(subset='train',
categories=categories, shuffle=True, random_state=42)
36. Natural Language Processing
Natural Language Processing
Analysing our dataset
The target_names holds the list of the requested category names:
>>> twenty_train.target_names
['alt.atheism', 'comp.graphics', 'sci.med',
'soc.religion.christian']
The files themselves are loaded in memory in the data attribute
>>> len(twenty_train.data)
2257
>>> len(twenty_train.filenames)
2257
37. Natural Language Processing
Natural Language Processing
Analysing our dataset
Content of the first lines of the first loaded file
>>> print("n".join(twenty_train.data[0].split("n")[:3]))
From: sd345@city.ac.uk (Michael Collier)
Subject: Converting images to HP LaserJet III?
Nntp-Posting-Host: hampton
The category integer id of each sample is stored in the target attribute
>>> twenty_train.target[:10]
array([1, 1, 3, 3, 3, 3, 3, 2, 2, 2])
38. Natural Language Processing
Natural Language Processing
Now we will apply the bag of word approach
Tokenizing text with scikit-learn
>>> from sklearn.feature_extraction.text import
CountVectorizer
>>> count_vect = CountVectorizer()
>>> X_train_counts =
count_vect.fit_transform(twenty_train.data)
>>> X_train_counts.shape
(2257, 35788)
39. Natural Language Processing
Natural Language Processing
Occurrence count is a good start but there is an issue -
● longer documents will have higher average count values than shorter
documents
To avoid these potential discrepancies we
● Divide the number of occurrences of each word in a document by the total
number of words in the document: these new features are called tf for
Term Frequencies.
40. Natural Language Processing
Natural Language Processing
How can we improve tf ?
Downscale weights for words that occur in many documents in the corpus and
are therefore less informative than those that occur only in a smaller portion of
the corpus.
This downscaling is called tf–idf for “Term Frequency times Inverse
Document Frequency”.
41. Natural Language Processing
Natural Language Processing - Tf-idf
● Tf-idf stands for term frequency-inverse document frequency
● Tf-idf weight is often used in
○ Information retrieval and
○ Text mining
● This weight is a used to evaluate
○ How important a word is to a
○ Document in a collection or corpus
42. Natural Language Processing
Natural Language Processing - Tf
Term Frequency
● Measures how frequently a term occurs in a document
● It is possible that a term would appear
○ Much more times in long documents than shorter ones
○ This is why we normalize TF
TF(t) = (Number of times term t appears in a document) / (Total
number of terms in the document)
43. Natural Language Processing
Natural Language Processing - IDF
Inverse Document Frequency
● Measures how important a term is
● In TF, all terms are considered equally important
● How ever some words and stop words appears lot of time
○ But have least importance
● In IDF we weight down frequent terms
○ And scale up rare terms
IDF(t) = log_e(Total number of documents / Number of
documents with term t in it)
44. Natural Language Processing
Natural Language Processing - Tf-idf
Example
● Consider a document containing 100 words and the word cat appears 3
times
● The term frequency(tf) for cat is
○ (3 / 100) = 0.03
45. Natural Language Processing
Natural Language Processing - Tf-idf
Example
● Now, assume we have 10 million documents and
○ The word cat appears in 1,000 of these
● The inverse document frequency(idf) is
○ log(10,000,000 / 1,000) = 4
● Tf-idf weight is the product of tf and idf
○ 0.03 * 4 = 0.12
46. Natural Language Processing
Natural Language Processing
Now let us apply tf-idf to our example
tfidf_transformer = TfidfTransformer()
>>> X_train_tfidf =
tfidf_transformer.fit_transform(X_train_counts)
>>> X_train_tfidf.shape
(2257, 35788)
47. Natural Language Processing
Natural Language Processing
Training a classifier
We’ll start with a naïve Bayes classifier, which provides a nice baseline
for this task.
>>> from sklearn.naive_bayes import MultinomialNB
>>> clf = MultinomialNB().fit(X_train_tfidf,
twenty_train.target)
The multinomial variant of Naive Bayes is one the most suitable for word
counts tasks.
48. Natural Language Processing
Natural Language Processing
Now let us make a prediction on a new document
To try to predict the outcome on a new document we need to extract the
features using almost the same feature extracting chain as before
● We will first transform the new document to count vectors
● Then we’ll transform it with the tfidf_transformer.
● Finally we’ll call the predict method of the classifier
49. Natural Language Processing
Natural Language Processing
>>> docs_new = ['God is love', 'OpenGL on the GPU is fast']
>>> X_new_counts = count_vect.transform(docs_new)
>>> X_new_tfidf = tfidf_transformer.transform(X_new_counts)
>>> predicted = clf.predict(X_new_tfidf)
>>> for doc, category in zip(docs_new, predicted):
... print('%r => %s' % (doc, twenty_train.target_names[category]))
...
'God is love' => soc.religion.christian
'OpenGL on the GPU is fast' => comp.graphics
Run it on Notebook
50. Natural Language Processing
Natural Language Processing
Now let us combine all the steps in the form of a pipeline
>>> from sklearn.pipeline import Pipeline
>>> text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB()),])
We can now train the model with a single command
>>> text_clf.fit(twenty_train.data, twenty_train.target)
Pipeline(...)
51. Natural Language Processing
Natural Language Processing
Now let us perform performance evaluation on the test set
>>> import numpy as np
>>> twenty_test = fetch_20newsgroups(subset='test',
... categories=categories, shuffle=True,
random_state=42)
>>> docs_test = twenty_test.data
>>> predicted = text_clf.predict(docs_test)
>>> np.mean(predicted == twenty_test.target)
0.834…
I.e., we achieved 83.4% accuracy
52. Natural Language Processing
Natural Language Processing
Now we’ll use a different classifier and compute the performance
metrics
>>> from sklearn.linear_model import SGDClassifier
>>> text_clf = Pipeline([('vect', CountVectorizer()),
... ('tfidf', TfidfTransformer()),
... ('clf', SGDClassifier(loss='hinge', penalty='l2',
... alpha=1e-3, random_state=42,
... max_iter=5, tol=None)),
... ])
53. Natural Language Processing
Natural Language Processing
Now we’ll use a different classifier and compute the performance
metrics
>>> text_clf.fit(twenty_train.data, twenty_train.target)
Pipeline(...)
>>> predicted = text_clf.predict(docs_test)
>>> np.mean(predicted == twenty_test.target)
0.912...
54. Natural Language Processing
Natural Language Processing
Parameter tuning using grid search
Since there are different parameters which we can choose, we’ll apply grid
search to find the best parameters
>>> parameters = {'vect__ngram_range': [(1, 1), (1, 2)],
... 'tfidf__use_idf': (True, False),
... 'clf__alpha': (1e-2, 1e-3),
... }
Here we’ll be applying grid search for the parameters - ngram_range,
use_idf and alpha.
55. Natural Language Processing
Natural Language Processing
Parameter tuning using grid search
If we have multiple CPU cores at our disposal, we can tell the grid searcher
to try these eight parameter combinations in parallel with the n_jobs
parameter.
>>> gs_clf = GridSearchCV(text_clf, parameters,
n_jobs=-1)
>>> gs_clf = gs_clf.fit(twenty_train.data[:400],
twenty_train.target[:400])
56. Natural Language Processing
Natural Language Processing
Predicting and finding best score
>>> twenty_train.target_names[gs_clf.predict(['God is
love'])[0]]
'soc.religion.christian'
>>> gs_clf.best_score_
0.900...
57. Natural Language Processing
Natural Language Processing
Predicting and finding best score
>>> for param_name in sorted(parameters.keys()):
... print("%s: %r" % (param_name,
gs_clf.best_params_[param_name]))
...
clf__alpha: 0.001
tfidf__use_idf: True
vect__ngram_range: (1, 1)
59. Natural Language Processing
Natural Language Processing - Stanford NLP
● Stanford CoreNLP provides a set of human language technology
tools.
● It can give
○ The base forms of words,
○ Their parts of speech,
○ Whether they are names of companies, people, etc.,
○ Mark up the structure of sentences in terms of phrases and
syntactic dependencies,
○ Indicate which noun phrases refer to the same entities, indicate
sentiment
60. Natural Language Processing
Natural Language Processing - Stanford NLP
Choose Stanford CoreNLP if you need:
● An integrated NLP toolkit with a broad range of grammatical
analysis tools
● A fast, robust annotator for arbitrary texts, widely used in
production
● A modern, regularly updated package, with the overall highest
quality text analytics
61. Natural Language Processing
Natural Language Processing - Stanford NLP
Choose Stanford CoreNLP if you need:
● Support for a number of major (human) languages
● Available APIs for most major modern programming languages
● Ability to run as a simple web service
62. Natural Language Processing
Natural Language Processing - Stanford NLP
Programming languages and operating systems
Stanford CoreNLP is written in Java; recent releases require Java
1.8+.
You can interact with CoreNLP via the command-line or its
web service using languages like Javascript, Python etc.
63. Natural Language Processing
Natural Language Processing - Stanford NLP
Programming languages and operating systems
You can use Stanford CoreNLP from
● The command-line, via its original Java programmatic API,
● Via the object-oriented simple API,
● Via third party APIs for most major modern programming languages,
Or via a web service.
It works on Linux, macOS, and Windows
64. Natural Language Processing
Natural Language Processing
More coming up on CloudxLab
● Word2vec - Vector Representations of Words
● Deep Learning - LSTM - Long Short-Term Memory
● GloVe - Global Vectors for Word Representation
● spaCY - Industrial-Strength Natural Language Processing in Python
● Hands-on using Stanford CoreNLP
● List of APIs available for chatbots etc