Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
5 Hour(s) 47 Minute(s)
Language
English
Taught by
Martin Jocqueviel, SuperDataScience Team, Ligency
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
1 views
Course Overview
Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colab
Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP.
Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automation, language modeling, event extraction, fraud detection on huge contracts are only a few examples of what is demanded today. Learning NLP is key to bring real solutions to the present and future needs.
Throughout this course, we will leverage the huge amount of speech and text data available online, and we will explore the main 3 and most powerful NLP applications, that will give you the power to successfully approach any real-world challenge.
First, we will dive into CNNs to create a sentimental analysis application.
Then we will go for Transformers, replacing RNNs, to create a language translation system.
The course is user-friendly and efficient: Modern NL leverages the latest technologies—Tensorflow 2.0 and Google Colab—assuring you that you won’t have any local machine/software version/compatibility issues and that you are using the most up-to-date tools.
Course Content
- 6 section(s)
- 39 lecture(s)
- Section 1 Introduction
- Section 2 CNN for NLP - Intuition
- Section 3 CNN for NLP - Application (sentimental analysis)
- Section 4 Transformer - Intuition
- Section 5 Transformer - Application
- Section 6 Congratulations!! Don't forget your Prize :)
What You’ll Learn
- Build a Transformer, new model created by Google, for any sequence to sequence task (e.g. a translator)
- Build a CNN specialized in NLP for any classification task (e.g. sentimental analysis)
- Write a custom training process for more advanced training methods in NLP
- Create customs layers and models in TF 2.0 for specific NLP tasks
- Use Google Colab and Tensorflow 2.0 for your AI implementations
- Pick the best model for each NLP task
- Understand how we get computers to give meaning to the human language
- Create datasets for AI from those data
- Clean text data
- Understand why and how each of those models work
- Understand everything about the attention mechanism, lying behind the newest and most powerful NLP algorithms
Reviews
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SShashank Yadahalli
good
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AAbhay Mandal
NA
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RRakesh Ranjan
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KKumari Rowani
good
