Deep Learning: Natural Language Processing with Transformers from Udemy

Deep Learning is a hot topic today. This is because of the impact it's having in several industries. One of the fields in which deep learning has the most influence today is Natural Language Processing.

To understand why Deep Learning based Natural Language Processing is so popular; it suffices to take a look at the different domains where giving a computer the power to understand and make sense out of text and generate text has changed our lives.

Some applications of Natural Language Processing are in:

Helping people around the world learn about any topic ChatGPT
Helping developers code more efficiently with Github Copilot.
Automatic topic recommendation in our Twitter feeds
Automatic Neural Machine Translation with Google Translate
E-commerce search engines like those of Amazon
Correction of Grammar with Grammarly

The demand for Natural Language Processing engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(

In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and Huggingface transformers (most popular NLP focused library ). We shall start by understanding how to build very simple models (like Linear regression model for car price prediction and RNN text classifiers for movie review analysis) using Tensorflow to much more advanced transformer models (like Bert, GPT, BlenderBot, T5, Sentence Transformers and Deberta).

After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep learning for NLP solutions that big tech companies encounter.

You will learn:

The Basics of Tensorflow (Tensors, Model building, training, and evaluation)

Text Preprocessing for Natural Language Processing.
Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.
Sentiment analysis with RNNs, Transformers, and Huggingface Transformers (Deberta)
Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5...)
Machine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)
Machine translation with RNNs, attention, transformers, and Huggingface Transformers (T5)
Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)
Intent Classification with Deberta in Huggingface transformers
Named Entity Relation with Roberta in Huggingface transformers
Neural Machine Translation with T5 in Huggingface transformers
Extractive Question Answering with Longformer in Huggingface transformers
E-commerce search engine with Sentence transformers
Lyrics Generator with GPT2 in Huggingface transformers
Grammatical Error Correction with T5 in Huggingface transformers
Elon Musk Bot with BlenderBot in Huggingface transformers
Speech recognition with RNNs

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals.

This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.

Enjoy.

What's inside

Learning objectives

The basics of tensors and variables with tensorflow
Mastery of the fundamentals of machine learning and the machine learning developmment lifecycle.
Basics of tensorflow and training neural networks with tensorflow 2.
Sentiment analysis with recurrent neural networks, attention models and transformers from scratch
Neural machine translation with recurrent neural networks, attention models and transformers from scratch
Recurrent neural networks, modern rnns, training sentiment analysis models with tensorflow 2.
Intent classification with deberta in huggingface transformers
Conversion from tensorflow to onnx model
Building api with fastapi

Deploying api to the cloud
Neural machine translation with t5 in huggingface transformers
Extractive question answering with longformer in huggingface transformers
E-commerce search engine with sentence transformers
Lyrics generator with gpt2 in huggingface transformers
Grammatical error correction with t5 in huggingface transformers
Elon musk bot with blenderbot in huggingface transformers
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The basics of tensors and variables with tensorflow
Mastery of the fundamentals of machine learning and the machine learning developmment lifecycle.
Basics of tensorflow and training neural networks with tensorflow 2.
Sentiment analysis with recurrent neural networks, attention models and transformers from scratch
Neural machine translation with recurrent neural networks, attention models and transformers from scratch
Recurrent neural networks, modern rnns, training sentiment analysis models with tensorflow 2.
Intent classification with deberta in huggingface transformers
Conversion from tensorflow to onnx model
Building api with fastapi
Deploying api to the cloud
Neural machine translation with t5 in huggingface transformers
Extractive question answering with longformer in huggingface transformers
E-commerce search engine with sentence transformers
Lyrics generator with gpt2 in huggingface transformers
Grammatical error correction with t5 in huggingface transformers
Elon musk bot with blenderbot in huggingface transformers
Show more
Show less

Syllabus

intro

Welcome

General Introduction

About this Course

Link to Code

[PRE-REQUISITE] Tensors and Variables

Text Preprocessing for Sentiment Analysis

Basics

Initialization and casting

Indexing

Maths Operations

Linear algebra operations

Common methods

Ragged tensors

Sparse tensors

String tensors

Variables

[PRE-REQUISITE] Building Neural Networks with TensorFlow

Link to Dataset

Task Understanding

Data Preparation

Advanced RNNs (LSTM and GRU)

Linear Regression Model

Error Sanctioning

Training and Optimization

Performance Measurement

Validation and Testing

Corrective Measures

TensorFlow Datasets

Understanding Sentiment Analysis

Text Standardization

Tokenization

One-hot encoding and Bag of Words

Term frequency - Inverse Document frequency (TF-IDF)

Embeddings

Sentiment Analysis with Recurrent neural networks

How Recurrent neural networks work

Building and training RNNs

1D Convolutional Neural Network

Sentiment Analysis with transfer learning

Understanding Word2vec

Integrating pretrained Word2vec embeddings

Testing

Visualizing embeddings

Neural Machine Translation with Recurrent Neural Networks

Understanding Machine Translation

Building, training and testing Model

Understanding BLEU score

Coding BLEU score from scratch

Neural Machine Translation with Attention

Understanding Bahdanau Attention

Building, training and testing Bahdanau Attention

Neural Machine Translation with Transformers

Understanding Transformer Networks

Building, training and testing Transformers

Building Transformers with Custom Attention Layer

Visualizing Attention scores

Sentiment Analysis with Transformers

Sentiment analysis with Transformer encoder

Sentiment analysis with LSH Attention

Transfer Learning and Generalized Language Models

Understanding Transfer Learning

Ulmfit

Gpt

Bert

Albert

Gpt2

Roberta

Sentiment Analysis with Deberta in Huggingface transformers

Building,training and testing model

Model Deployment

Understanding Distillation

Finetuning Distilled Model

Converting Tensorflow model to Onnx format

Understanding quantization

Practical quantization of Onnx model

What is an API

Building an API with FastAPI

Deploying API to cloud

Intent Classification with Deberta in Huggingface transformers

Problem Understanding and Data Preparation

Named Entity Relation with Roberta in Huggingface transformers

Neural Machine Translation with T5 in Huggingface transformers

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a decent foundation for beginners seeking to break into deep learning and NLP

Offers hands-on projects and interactive materials, making it suitable for learners who prefer a practical approach

Covers a comprehensive range of NLP concepts, including sentiment analysis, machine translation, and named entity recognition

Leverages industry-standard tools like TensorFlow and HuggingFace Transformers, providing learners with relevant skills

Course instructors have a strong reputation in the field of deep learning and NLP, lending credibility to the course content

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Learning: Natural Language Processing with Transformers with these activities:

Review the fundamentals of Tensorflow

Show steps

Review the basics to prepare you for the course

Browse courses on Machine Learning

Show steps

Review the documentation of key tensorflow concepts
Download the Tensorflow library and practice building simple models

Volunteer for projects related to NLP or Machine Learning

Show steps

Gain practical experience and contribute to the community

Browse courses on Natural Language Processing

Show steps

Find volunteering opportunities through organizations
Offer skills in NLP or Machine Learning
Work on real-world projects

Find a mentor in the field of NLP

Show steps

Seek guidance from experienced professionals in NLP

Browse courses on Natural Language Processing

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Attend industry events or join online communities focused on NLP
Network with professionals and identify potential mentors

Five other activities

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Attend workshops for NLP using Tensorflow

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Learn about building advanced NLP models using Tensorflow

Browse courses on TensorFlow

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Find workshops and tutorials on Tensorflow and NLP
Attend workshops or tutorials
Implement the concepts covered in the workshop

Predict sentiment for movie reviews

Show steps

Practice sentiment analysis with RNNs and Transformers

Browse courses on Sentiment Analysis

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Import the required libraries including Tensorflow and Huggingface transformers
Load and preprocess the movie review dataset
Build and train RNN and Transformer models for sentiment analysis
Evaluate the performance of the models

Participate in NLP competitions or hackathons

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Put your skills to the test and gain experience in solving real-world NLP problems

Browse courses on Natural Language Processing

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Identify NLP competitions or hackathons
Form a team or participate individually
Develop innovative NLP solutions

Practice building and training Machine Learning models

Show steps

Gain hands-on experience with ML models beyond the course

Browse courses on Machine Learning

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Find a platform like Leetcode or Kaggle
Solve practice problems and challenges
Implement solutions using Tensorflow or other relevant libraries

Mentor junior developers or students interested in NLP

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Reinforce your knowledge and understanding by teaching others

Browse courses on Natural Language Processing

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Reach out to junior developers or students seeking guidance in NLP
Offer guidance, share resources, and answer questions

Career center

Learners who complete Deep Learning: Natural Language Processing with Transformers will develop knowledge and skills that may be useful to these careers:

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Deep Learning

Natural Language Processing with Transformers

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