Home>Courses>Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow

Deep learning has revolutionized NLP and problems that require a large amount of work in terms of new features. Tuning can now be efficiently solved using NLP.

Intermediate

122 Lessons

15h

Certificate of Completion

Deep learning has revolutionized NLP and problems that require a large amount of work in terms of new features. Tuning can now be efficiently solved using NLP.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

33 Playgrounds

10 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

Recommendations

Course Overview

Deep learning has revolutionized natural language processing (NLP) and NLP problems that require a large amount of work in terms of designing new features. Tuning models can now be efficiently solved using NLP. In this course, you will learn the fundamentals of TensorFlow and Keras, which is a Python-based interface for TensorFlow. Next, you will build embeddings and other vector representations, including the skip-gram model, continuous bag-of-words, and Global Vector representations. You will then learn ...Show More

What You'll Learn

Familiarity with word embeddings, including the skip-gram model, continuous bag-of-words (CBOW), and Global Vector representations (GloVe)

Understanding of deep models like CNNs, RNNs, LSTMs, and GRUs

Hands-on experience building NLP tools using TensorFlow, Keras, and Hugging Face libraries

Working knowledge of transformers and their variants in sequence-to-sequence modeling

Familiarity with word embeddings, including the skip-gram model, continuous bag-of-words (CBOW), and Global Vector representations (GloVe)

Course Content

Introduction to Natural Language Processing

6 Lessons

Get familiar with NLP tasks, traditional approaches, and deep learning advancements.

Introduction: Natural Language Processing

What Is Natural Language Processing?

Tasks of Natural Language Processing

The Traditional Approach to Natural Language Processing

The Deep Learning Approach to Natural Language Processing

Summary: Introduction to Natural Language Processing

Understanding TensorFlow 2

14 Lessons

Get started with TensorFlow 2's architecture, operations, data pipelines, and Keras API for neural networks.

Introduction: Understanding TensorFlow 2

What Is TensorFlow 2?

TensorFlow 2 Architecture: Building and Executing Graphs

Café Le TensorFlow 2: Understanding TensorFlow 2 with an Analogy

Flashback: TensorFlow 1

Defining Inputs in TensorFlow

Building a Data Pipeline Using the tf.data API

Defining Variables and Output in TensorFlow

Defining Operations in TensorFlow

Neural Network-Related Operations

Keras: TensorFlow's Model-Building API

Implementing Our First Neural Network

Summary: Understanding TensorFlow 2

Quiz: Understanding TensorFlow 2

Word2vec: Learning Word Embeddings

10 Lessons

Examine Word2vec and classical methods for word representation, with TensorFlow implementation.

Introduction of Word2vec: Learning Word Embeddings

Classical Approaches to Learning Word Representations

An Intuitive Understanding of Word2vec

The Skip-Gram Algorithm

Data Generators in the Skip-Gram Algorithm with TensorFlow

Implementing the Skip-Gram Architecture with TensorFlow

Training and Evaluating the Skip-Gram Model

The Continuous Bag-of-Words Algorithm

Summary of Word2vec: Learning Word Embeddings

Quiz of Word2vec: Learning Word Embeddings

Advanced Word Vector Algorithms

13 Lessons

Grasp the fundamentals of advanced word vector algorithms like GloVe and ELMo for NLP.

Introduction: Advanced Word Vector Algorithms

GloVe: Global Vectors Representation

Implementing GloVe

Generating Data for GloVe

Training and Evaluating GloVe

ELMo: Taking Ambiguities Out of Word Vectors

Preparing Inputs for ELMo

Generating Embeddings with ELMo

Dataset in Document Classification with ELMo

Generating Document Embeddings

Classifying Documents with Document Embeddings

Summary: Advanced Word Vector Algorithms

Quiz: Advanced Word Vector Algorithms

Sentence Classification with Convolutional Neural Networks

13 Lessons

Dive into CNNs for image and sentence classification with TensorFlow, emphasizing practical applications.

Introduction: Sentence Classification with CNNs

Introducing CNNs

Understanding CNNs: Convolution Operations

Understanding CNNs: Pooling Operations

Understanding CNNs: Fully Connected Layers

Exercise: Image Classification on Fashion-MNIST with CNN

CNNs for Sentence Classification: Transformation of Data

CNNs for Sentence Classification: Downloading and Preparing Data

CNNs for Sentence Classification: Building a Tokenizer

The Sentence Classification CNN Model

Sentence Classification with CNNs

Summary: Sentence Classification with CNNs

Quiz: Sentence Classification with CNNs

Recurrent Neural Networks

10 Lessons

Focus on Recurrent Neural Networks' structure, training, and applications in sequence data tasks.

Introduction: Recurrent Neural Networks

Understanding RNNs

Backpropagation through Time

Applications of RNNs

Named Entity Recognition with RNNs: Preparing Data

Named Entity Recognition with RNNs: Defining the Model

Named Entity Recognition with RNNs: Training and Evaluation

NER with Character and Token Embeddings

Summary: Recurrent Neural Networks

Quiz: Recurrent Neural Networks

Understanding Long Short-Term Memory Networks

7 Lessons

Build on LSTM networks to handle short-term and long-term dependencies effectively.

Introduction: Understanding Long Short-Term Memory Networks

Understanding Long Short-Term Memory Networks

How LSTMs Solve the Vanishing Gradient Problem

Improving LSTMs

Other Variants of LSTMs

Summary: Understanding Long Short-Term Memory Networks

Quiz: Understanding Long Short-Term Memory Networks

Applications of LSTM: Generating Text

9 Lessons

Learn how to use LSTMs, GRUs, and beam search for efficient text generation.

Introduction: Applications of LSTMs—Generating Text

Understanding the Data

Implementing the Language Model

Generating New Text with the Model

Comparing LSTMs to LSTMs with Peephole Connections and GRUs

Improving Sequential Models: Beam Search

Improving LSTMs: Generating Text with Words Instead of N-grams

Summary: Applications of LSTMs—Generating Text

Quiz: Applications of LSTMs—Generating Text

Sequence-to-Sequence Learning: Neural Machine Translation

13 Lessons

Discover the logic behind sequence-to-sequence learning for neural machine translation, including model architecture, data preparation, training, and evaluation.

Introduction: Sequence-to-Sequence Learning—NMT

Machine Translation

Understanding Neural Machine Translation

Preparing Data for the NMT System

Defining the NMT Model

Attention: Analyzing, Computing, and Implementing

Training the NMT

The BLEU Score: Evaluating Machine Translation Systems

Visualizing Attention Patterns

Inference with NMT

Other Applications of Seq2Seq Models: Chatbots

Summary: Sequence-to-Sequence Learning—NMT

Quiz: Sequence-to-Sequence Learning—NMT

10.

Transformers

10 Lessons

Master the steps to leverage transformers and BERT for NLP tasks and question answering.

Introduction: Transformers

Transformer Architecture: Encoder, Decoder, and Computing Output

Transformer Architecture: Embedding Layers

Transformer Architecture: Residuals and Normalization

Understanding BERT

Use Case: Using BERT to Answer Questions

Use Case: Implementing BERT

Training and Evaluating Model and Answering Questions with BERT

Summary: Transformers

Quiz: Transformers

11.

Image Captioning with Transformers

10 Lessons

Enhance your skills in image captioning with transformer models using deep learning techniques.

Introduction: Image Captioning with Transformers

Getting to Know the Data

Processing and Tokenizing Data

Defining a tf.data.Dataset

The Machine Learning Pipeline for Image Caption Generation

Implementing and Training the Model with TensorFlow

Evaluating the Results Quantitatively

Evaluating the Model and Generating Captions from It

Summary: Image Captioning with Transformers

Quiz: Image Captioning with Transformers

12.

Final Remarks

1 Lessons

Deepen your knowledge of NLP principles, TensorFlow models, neural networks, and key algorithms.

Wrap Up

13.

Appendix: Mathematical Foundations and Advanced TensorFlow

6 Lessons

Investigate essential mathematical tools, data structures, and TensorBoard for understanding NLP with TensorFlow.

Introduction to the Technical Tools

Basic Data Structures

Special Types of Matrices

Probability

Visualizing Word Embeddings with TensorBoard

Summary: Appendix

Course Author

Packt

Project

Predictive Time Series Analysis Using LSTM and Flask

intermediate

1 hour 30 min

Project

Predictive Time Series Analysis Using LSTM and Flask

Learn to forecast time series with LSTM and deploy using Flask and Docker for Bitcoin price data analysis.

intermediate

1 hour 30 mins

Trusted by 2.5 million developers working at companies

"These are high-quality courses. Trust me. I own around 10 and the price is worth it for the content quality. EducativeInc came at the right time in my career. I'm understanding topics better than with any book or online video tutorial I've done. Truly made for developers. Thanks"

Anthony Walker

@_webarchitect_

"Just finished my first full #ML course: Machine learning for Software Engineers from Educative, Inc. ... Highly recommend!"

Evan Dunbar

ML Engineer

"You guys are the gold standard of crash-courses... Narrow enough that it doesn't need years of study or a full blown book to get the gist, but broad enough that an afternoon of Googling doesn't cut it."

Carlos Matias La Borde

Software Developer

"I spend my days and nights on Educative. It is indispensable. It is such a unique and reader-friendly site"

Souvik Kundu

Front-end Developer

"Your courses are simply awesome, the depth they go into and the breadth of coverage is so good that I don't have to refer to 10 different websites looking for interview topics and content."

Vinay Krishnaiah

Software Developer

"I've tried probably 5-7 different sites and Educative is easily the best. It perfectly blends explanation with interactivity"

Eric Downs

Musician/Entrepeneur

Hands-on Learning Powered by AI

See how Educative uses AI to make your learning more immersive than ever before.

Instant Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

AI-Powered Mock Interviews

Put your skills to the test in a simulated interview setting. Receive personalized feedback based on your performance. Available in Premium & Premium Plus plans.

Adaptive Learning

At various checkpoints throughout Educative courses, you will be prompted to take a quick assessment. Receive a condensed curriculum tailored to your strengths and skill gaps.

Explain with AI

Select any text within any Educative course, and get an instant explanation — without ever leaving your browser.

AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!

Recommended before starting this course

Course

Performing Natural Language Processing with R

intermediate

9 hours

Course

Text Preprocessing with Python

This course equips learners with essential skills to prepare text data for further analysis.

intermediate

16 hours

Course

Simplifying Machine Learning with PyCaret in Python