Natural Language Processing with Python
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural-language generation. Python language has always been considered importantly for NLP.
About Deep Learning
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
Python is an interpreted, high-level, general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.
About this Course on deep learning for natural language processing with python
In this course, you are going to look at deep learning for natural language processing with python. Moreover, they are going to show you how to do even more awesome things. You will learn not just 1, but 4 new architectures in this course.
First up is word2vec. In this course, they will show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know. It is interesting because it magically maps words to a vector space where you can find analogies, like:
king – man = queen – woman France – Paris = England – London December – November = July – June
For those beginners who find algorithms tough and just want to use a library, You will be demonstrated with the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers. It will also cover the look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.
Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train. Also, you will be able to look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.
All of the materials required for this course can be downloaded and installed for FREE. They will do most of their work in Numpy, Matplotlib, and Theano and always will be available to answer your questions and help you along your data science journey.
This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
What you will learn from this course:
- In natural language processing with python course, you will be able to understand and implement word2vec and the skip-gram method in word2vec.
- Implementation of GloVe using gradient descent and alternating least squares.
- Use recurrent neural networks for named entity recognition.
- Use recurrent neural networks for named entity recognition.
- Understand the CBOW method in word2vec.
- Understand the negative sampling optimization in word2vec.
- Use recurrent neural networks for parts-of-speech tagging.
- Understand and implement recursive neural networks for sentiment analysis.
- Use Gensim to obtain pre-trained word vectors and compute similarities and analogies.
In this course on natural language processing with python, you will be going through:
1. Beginner’s corner: Working with Word Vector
- What is a word analogy?
- Trying to find and assess word vectors using TF-IDF and t-SNE.
- Pretrained word vectors from GloVe and word2vec
- Text Classification with word vectors and Text Classification in Code.
- Using pre-trained vectors later in the course.
2. Review of Language Modeling and Neural Network
- Bigrams and Language Models and Bigrams in Code.
- Neural Bigram Model and Model in Code.
- Neural Network Bigram Model and Bigram Model in Code.
- Improving Efficiency and Efficiency in Code.
3. Word Embedding and Word2Vec
- Return of the Bigram, CBOW, Skip-Gram, Hierarchical Softmax.
- Negative Sampling and Sampling – Important Details.
- Why do I have 2 word embedding matrices and what do I do with them?
- Word2Vec implementation tricks and implementation outline and Word2Vec in Code with Numpy.
- Tensor flow or Theano
- Word2Vec Tensor flow Implementation Details and Word2Vec Tensor flow in Code.
4. Word Embedding Using GloVe
- GloVe Section Introduction.
- Matrix Factorization for Recommender Systems – Basic Concepts.
- Matrix Factorization Training, Expanding the Matrix Factorization Model.
- Regularization for Matrix Factorization and GloVe – Global Vectors for Word Representation.
- GloVe in Code – Numpy Gradient Descent and Alternating Least Squares and GloVe in Tensor flow with Gradient Descent.
- Visualizing country analogies with t-SNE and Hyper parameter Challenge.
5. Unifying Word2Vec and GloVe
- Point wise Mutual Information – Word2Vec as Matrix Factorization.
- PMI in Code.
6. Using Neural Networks to Solve NPL Problems
- Parts-of-Speech (POS) Tagging.
- How can neural networks be used to solve POS tagging?
- Parts-of-Speech Tagging Recurrent Neural Network in Theano and Parts-of-Speech Tagging Recurrent Neural Network in Tensor flow.
- Named Entity Recognition Baseline, Named Entity Recognition RNN in Theano and Named Entity Recognition RNN in Tensor flow with Hyperparameter Challenge II.
7. Recursive Neural Network(Three Neural Networks)
- Recursive Neural Networks Section Introduction.
- Data Description for Recursive Neural Networks.
- What are Recursive Neural Networks / Tree Neural Networks (TNNs), building a TNN with Recursion and Trees to Sequences?
- Recursive Neural Tensor Networks and RNTN in Tensor flow Tips and codes.
- Recursive Neural Network in Tensor Flow with Recursion.
8. Theano and Tensor flow Basic Review
- Theano Basics and Neural Network in Code.
- Tensor flow Basics and Neural Network in Code.
- For the natural language processing with python course, learner needs basic understanding of backpropagation and gradient descent.
- Also, be able to derive and code the equations on your own
- Code a recurrent neural network from basic primitives in Theano (or Tensor flow), especially the scan function
- Code a feed-forward neural network in Theano (or Tensor flow)
- Helpful to have experience with tree algorithms
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- Awesome tutorial on word embedding (word2vec and glove) techniques.
- You will find it more advanced than any other course platforms.
- Focus on demonstration of core parts of algorithm’s.
- Lengthy preliminary content.
- It should have detail practical information.
Specification: Natural Language Processing with Deep Learning in Python