Course: Deep Learning for NLP
duration: 15 hours |
Language: English (US) |
access duration: 90 days |
Details
In recent times, natural language processing (NLP) has seen many advancements, most of which are in deep learning models. NLP as a problem is very complicated, and deep learning models can handle that scale and complication with many different variations of neural network architecture. How this works in practice is what you´ll learn during this course. You start with the basics of NLP and soon you move on to Neural Network Architectures (NNA). NNA provides a method of processing language-based information to solve complex data-driven problems. Next, you’ll learn the basics of memory-based networks and how to implement TensorFlow to handle extended context in languages.
The essential aspect of human intelligence is our learning processes, constantly augmented with the transfer of concepts and fundamentals. This course will help you learn the fundamentals of transfer learning for NLP, its various challenges, and use cases. Finally, you’ll explore GitHub bug prediction analyses to solve real-world problems.
Result
After completing this course, you will be able to use the essential fundamentals of deep learning for NLP and outline its various industry use cases, frameworks, fundamental sentiment analysis problems and a product classification dataset to implement neural networks for NLP problems. Furthermore, you will have learned the basics of memory-based networks and their implementation in TensorFlow to understand the effect of memory and more extended context for NLP datasets. You will also understand the transfer learning methodology of solving NLP problems and be able to experiment with various models in TensorFlow, as well as, how to solve industry-level problems using deep learning methodology in the TensorFlow ecosystem.
Prerequisites
No formal prerequisites. However, some prior knowledge about the topic is recommended.
Target audience
Data analist
Content
Deep Learning for NLP
Deep Learning for NLP: Introduction
In recent times, natural language processing (NLP) has seen many
- advancements, most of which are in deep learning models. NLP as a
- problem is very complicated, and deep learning models can handle
- that scale and complication with many different variations of
- neural network architecture. Deep learning also has a broad
- spectrum of frameworks that supports NLP problem solving
- out-of-the-box. Explore the basics of deep learning and different
- architectures for NLP-specific problems. Examine other use cases
- for deep learning NLP across industries. Learn about various tools
- and frameworks used such as - Spacy, TensorFlow, PyTorch, OpenNMT,
- etc. Investigate sentiment analysis and explore how to solve a
- problem using various deep learning steps and frameworks. Upon
- completing this course, you will be able to use the essential
- fundamentals of deep learning for NLP and outline its various
- industry use cases, frameworks, and fundamental sentiment analysis
- problems.
Deep Learning for NLP: Neural Network Architectures
Natural language processing (NLP) is constantly evolving with
- cutting edge advancements in tools and approaches. Neural network
- architecture (NNA) supports this evolution by providing a method of
- processing language-based information to solve complex data-driven
- problems. Explore the basic NNAs relevant to NLP problems. Learn
- different challenges and use cases for single-layer perceptron,
- multi-layer perceptron, and RNNs. Analyze data and its distribution
- using pandas, graphs, and charts. Examine word vector
- representations using one-hot encodings, Word2vec, and GloVe and
- classify data using recurrent neural networks. After you have
- completed this course, you will be able to use a product
- classification dataset to implement neural networks for NLP
- problems.
Deep Learning for NLP: Memory-based Networks
In the journey to understand deep learning models for natural
- language processing (NLP), the subsequent iterations are
- memory-based networks, which are much more capable of handling
- extended context in languages. While basic neural networks are
- better than machine learning (ML) models, they still lack in more
- significant and large language data problems. In this course, you
- will learn about memory-based networks like gated recurrent unit
- (GRU) and long short-term memory (LSTM). Explore their
- architectures, variants, and where they work and fail for NLP.
- Then, consider their implementations using product classification
- data and compare different results to understand each
- architecture's effectiveness. Upon completing this course, you will
- have learned the basics of memory-based networks and their
- implementation in TensorFlow to understand the effect of memory and
- more extended context for NLP datasets.
Deep Learning for NLP: Transfer Learning
The essential aspect of human intelligence is our learning
- processes, constantly augmented with the transfer of concepts and
- fundamentals. For example, as a child, we learn the basic alphabet,
- grammar, and words, and through the transfer of these fundamentals,
- we can then read books and communicate with people. This is what
- transfer learning helps us achieve in deep learning as well. This
- course will help you learn the fundamentals of transfer learning
- for NLP, its various challenges, and use cases. Explore various
- transfer learning models such as ELMo and ULMFiT. Upon completing
- this course, you will understand the transfer learning methodology
- of solving NLP problems and be able to experiment with various
- models in TensorFlow.
Deep Learning for NLP: GitHub Bug Prediction Analysis
- Get down to solving real-world GitHub bug prediction problems in
- this case study course. Examine the process of data and library
- loading and perform basic exploratory data analysis (EDA) including
- word count, label, punctuation, and stop word analysis. Explore how
- to clean and preprocess data in order to use vectorization and
- embeddings and use counter vector and term frequency-inverse
- document frequency (TFIDF) vectorization methods with
- visualizations. Finally, assess different classifiers like logistic
- regression, random forest, or AdaBoost. Upon completing this
- course, you will understand how to solve industry-level problems
- using deep learning methodology in the TensorFlow ecosystem.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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