Szkolenia NLP w Zielona Góra

NLP Training in Zielona Góra
Natural Language Processing, Natural Language Processing courses

Zielona Góra, ul. Reja 6

sale szkoleniowe NobleProg
ul. Reja 6
65-076 Zielona Góra , LU
Poland
Lubuskie PL
Zielona Góra, ul. Reja 6

NLP Course Events - Zielona Góra

Kod Nazwa Miejscowość Czas trwania Data Kursu PHP Cena szkolenia [Zdalne / Stacjonarne]
nlp Natural Language Processing Zielona Góra, ul. Reja 6 21 hours pon., 2017-02-20 09:00 7200PLN / 3150PLN
python_nltk Natural Language Processing with Python Zielona Góra, ul. Reja 6 28 hours wt., 2017-03-07 09:00 27780PLN / 9418PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Zielona Góra, ul. Reja 6 28 hours pon., 2017-03-13 09:00 30980PLN / 10388PLN
python_nltk Natural Language Processing with Python Zielona Góra, ul. Reja 6 28 hours wt., 2017-05-09 09:00 27780PLN / 9418PLN
nlp Natural Language Processing Zielona Góra, ul. Reja 6 21 hours śr., 2017-05-10 09:00 7200PLN / 3150PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Zielona Góra, ul. Reja 6 28 hours wt., 2017-05-30 09:00 30980PLN / 10388PLN
nlp Natural Language Processing Zielona Góra, ul. Reja 6 21 hours wt., 2017-07-04 09:00 7200PLN / 3150PLN
python_nltk Natural Language Processing with Python Zielona Góra, ul. Reja 6 28 hours wt., 2017-07-18 09:00 27780PLN / 9418PLN
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example Zielona Góra, ul. Reja 6 28 hours pon., 2017-07-24 09:00 30980PLN / 10388PLN

Plany Kursów

Kod Nazwa Czas trwania Spis treści
Neuralnettf Neural Networks Fundamentals using TensorFlow as Example 28 hours

This course will give you knowledge in neural networks and generally in machine learning algorithm,  deep learning (algorithms and applications).

This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.

TensorFlow Basics

 Creation, Initializing, Saving, and Restoring TensorFlow variables

 Feeding, Reading and Preloading TensorFlow Data

 How to use TensorFlow infrastructure to train models at scale

 Visualizing and Evaluating models with TensorBoard

 

TensorFlow Mechanics

 Inputs and Placeholders

 Build the GraphS

o Inference

o Loss

o Training

 Train the Model

o The Graph

o The Session

o Train Loop

 Evaluate the Model

o Build the Eval Graph

o Eval Output

 

The Perceptron

 Activation functions

 The perceptron learning algorithm

 Binary classification with the perceptron

 Document classification with the perceptron

 Limitations of the perceptron

 

From the Perceptron to Support Vector Machines

 Kernels and the kernel trick

 Maximum margin classification and support vectors

 

Artificial Neural Networks

 Nonlinear decision boundaries

 Feedforward and feedback artificial neural networks

 Multilayer perceptrons

 Minimizing the cost function

 Forward propagation

 Back propagation

 Improving the way neural networks learn

 

Convolutional Neural Networks

 Goals

 Model Architecture

 Principles

 Code Organization

 Launching and Training the Model

 Evaluating a Model

python_nltk Natural Language Processing with Python 28 hours This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.

Overview of Python packages related to NLP

 

Introduction to NLP (examples in Python of course)

  1. Simple Text Manipulation
    1. Searching Text
    2. Counting Words
    3. Splitting Texts into Words
    4. Lexical dispersion
  2. Processing complex structures
    1. Representing text in Lists
    2. Indexing Lists
    3. Collocations
    4. Bigrams
    5. Frequency Distributions
    6. Conditionals with Words
    7. Comparing Words (startswith, endswith, islower, isalpha, etc...)
  3. Natural Language Understanding
    1. Word Sense Disambiguation
    2. Pronoun Resolution
  4. Machine translations (statistical, rule based, literal, etc...)
  5. Exercises

NLP in Python in examples

  1. Accessing Text Corpora and Lexical Resources
    1. Common sources for corpora
    2. Conditional Frequency Distributions
    3. Counting Words by Genre
    4. Creating own corpus
    5. Pronouncing Dictionary
    6. Shoebox and Toolbox Lexicons
    7. Senses and Synonyms
    8. Hierarchies
    9. Lexical Relations: Meronyms, Holonyms
    10. Semantic Similarity
  2. Processing Raw Text
    1. Priting
    2. struncating
    3. extracting parts of string
    4. accessing individual charaters
    5. searching, replacing, spliting, joining, indexing, etc...
    6. using regular expressions
    7. detecting word patterns
    8. stemming
    9. tokenization
    10. normalization of text
    11. Word Segmentation (especially in Chinese)
  3. Categorizing and Tagging Words
    1. Tagged Corpora
    2. Tagged Tokens
    3. Part-of-Speech Tagset
    4. Python Dictionaries
    5. Words to Propertieis mapping
    6. Automatic Tagging
    7. Determining the Category of a Word (Morphological, Syntactic, Semantic)
  4. Text Classification (Machine Learning)
    1. Supervised Classification
    2. Sentence Segmentation
    3. Cross Validation
    4. Decision Trees
  5. Extracting Information from Text
    1. Chunking
    2. Chinking
    3. Tags vs Trees
  6. Analyzing Sentence Structure
    1. Context Free Grammar
    2. Parsers
  7. Building Feature Based Grammars
    1. Grammatical Features
    2. Processing Feature Structures
  8. Analyzing the Meaning of Sentences
    1. Semantics and Logic
    2. Propositional Logic
    3. First-Order Logic
    4. Discourse Semantics
  9.  Managing Linguistic Data 
    1. Data Formats (Lexicon vs Text)
    2. Metadata
nlp Natural Language Processing 21 hours

This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.

The course will cover how to make use of text written by humans, such as  blog posts, tweets, etc...

For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.

Short Introduction to NLP methods

  • word and sentence tokenization
  • text classification
  • sentiment analysis
  • spelling correction
  • information extraction
  • parsing
  • meaning extraction
  • question answering

Overview of NLP theory

  • probability
  • statistics
  • machine learning
  • n-gram language modeling
  • naive bayes
  • maxent classifiers
  • sequence models (Hidden Markov Models)
  • probabilistic dependency
  • constituent parsing
  • vector-space models of meaning

Other regions

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