Natural Language Processing - Deep Learning Models in Python: Master NLP with cutting-edge deep learning techniques using Python.

Lazy Programmer

€82.99

Video Jun 2024 6hrs 29mins 1st Edition

Lazy Programmer

€82.99

Video Jun 2024 6hrs 29mins 1st Edition

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Key benefits

Comprehensive coverage of NLP using deep learning models.
Step-by-step coding exercises in TensorFlow.
Practical applications of text classification, embeddings, and named entity recognition.

Description

Embark on a journey into Natural Language Processing (NLP) with a focus on deep learning models using Python. The course starts with an introduction to neurons, explaining how they form the basic building blocks of neural networks. You will learn to fit lines and prepare classification codes, culminating in practical text classification tasks using TensorFlow. Progressing to Feedforward Artificial Neural Networks (ANNs), you will delve into forward propagation, activation functions, and multiclass classification. The course includes extensive code preparation for text classification in TensorFlow, covering text preprocessing, embeddings, and advanced techniques like Continuous Bag of Words (CBOW). This section ensures you understand the geometrical aspects and hyperparameter tuning. The course then explores Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), crucial for advanced NLP tasks. You will learn the intricacies of convolutions, CNN architecture, and their application to text. The RNN section covers simple RNNs, GRUs, and LSTMs, with hands-on exercises in text classification, parts-of-speech tagging, and named entity recognition in TensorFlow. Each section is designed to build your skills progressively, ensuring a deep understanding of both theoretical concepts and practical applications.

Who is this book for?

This course is designed for data scientists, machine learning engineers, and AI enthusiasts with a basic understanding of Python and machine learning. Familiarity with basic neural network concepts is beneficial but not mandatory.