Natural Language Processing(NLP) with Deep Learning in Keras
Natural Language Processing(NLP) with Deep Learning in Keras
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 3.98 GB
Genre: eLearning Video | Duration: 45 lectures (8 hours, 19 mins) | Language: English
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 3.98 GB
Genre: eLearning Video | Duration: 45 lectures (8 hours, 19 mins) | Language: English
Word2Vec, Glove, FastText, Universal Sentence Encoder, GRU, LSTM, Conv-1D, Seq2Seq, Machine Translation and much more!
What you'll learn
Upgrade the knowledge of Natural Language Processing using Deep Learning models
Requirements
Machine Learning, NLP basics, Linear Algebra, Python, Tensor Flow, Keras
Description
Natural Language Processing (NLP) is a hot topic into Machine Learning field.
This course is an advanced course of NLP using Deep Learning approach. Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course.
This course starts with the configuration and the installation of all resources needed including the installation of Tensor Flow CPU/GPU, Cuda and Keras. You will be able to use your GPU card if you have one, to accelate so fast the processes. But if you dont have a GPU card you can follow the instructions for running the standard CPU code, it will take a while but you still can run it.
After that we are going to review the main concepts of Deep Learning in the Chapter 2 for applying them into the Natural Language Processing field offering you a solid background for the main chapter.
In the main Chapter 3 we are going to study the main Deep Learning libraries and models for NLP such as Word Embeddings, Word2Vec, Glove, FastText, Universal Sentence Encoder, RNN, GRU, LSTM, Convolutions in 1D, Seq2Seq, Memory Networks, and the Attention mechanism.
This course offers you many examples, with different datasets suchs as Google News, Yelp comments, Amazon reviews, IMDB reviews, the Bible corpus, etc and different text corpus. At the final in Chapter 4 you will put in practice your knowledge with practical applications such as Multiclass Sentiment Analysis, Text Generation, Machine Translation, Developing a ChatBot and more.
For coding we are going to use TensorFlow, Keras, Google Colab and many Python libraries.
If you need a previous background in Natural Language Processing or in Machine Learning I recommend you my courses:
Python for Machine Learning and Data Mining or
Natural Language Processing with Python and NLTK
Who this course is for:
Professionals looking for an advanced course of Natural Language Processing using Deep Learning approach
Natural Language Processing(NLP) with Deep Learning in Keras
