Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues.
Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain.
The course goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs.
As you advance through this deep learning course, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.
If you are an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is the course for you.
A good working knowledge of Python, linear algebra and machine learning is a must.
By the end of this course, students will be able to:
Course attendance certificate issued by Semos Education
Starting with the basics, this book teaches you how to choose from the various text pre-processing techniques and select the best model from the several neural network architectures for NLP issues.
Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain.
The course goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs.
As you advance through this deep learning course, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.
If you are an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is the course for you.
A good working knowledge of Python, linear algebra and machine learning is a must.
By the end of this course, students will be able to:
Course attendance certificate issued by Semos Education
After completing my studies at Semos Education, I envision myself working in a technology company, such as Samsung, Apple, or a company of similar caliber.
Because Artificial Intelligence is the challenge of the future. With the modernization of lifestyles and technological advancements on a global scale, artificial intelligence is increasingly playing a key role in all aspects of life and development in society.
With taking the first step and investing enough effort, everyone can master this modern topic and stand out in the job market in one of the fastest-growing industries in the world.
The final project taught me many useful things, beyond the realm of video games. However, it greatly helped me get an idea of how machines 'learn' and how powerful they can be.
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