Deep Learning: Natural Language Processing With Transformers

Use Huggingface transformers and Tensorflow to build Sentiment analysis, Translation, Q&A, Search, Speech,… projects

What you'll learn

he 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

Requirements

Basic Math

No Programming experience.

Description

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 ChatGPTHelping developers code more efficiently with Github Copilot.Automatic topic recommendation in our Twitter feedsAutomatic Neural Machine Translation with  Google TranslateE-commerce search engines like those of AmazonCorrection of Grammar with GrammarlyThe 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 There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts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 transformersNamed Entity Relation with Roberta in Huggingface transformersNeural Machine Translation with T5 in Huggingface transformersExtractive Question Answering with Longformer in Huggingface transformersE-commerce search engine with Sentence transformersLyrics Generator with GPT2 in Huggingface transformersGrammatical Error Correction with T5 in Huggingface transformersElon Musk Bot with BlenderBot in Huggingface transformersSpeech recognition with RNNsIf 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!!!

Overview

Section 1: intro

Lecture 1 Welcome

Lecture 2 General Introduction

Lecture 3 About this Course

Section 2: [PRE-REQUISCITE] Tensors and Variables

Lecture 4 Basics

Lecture 5 Initialization and casting

Lecture 6 Indexing

Lecture 7 Maths Operations

Lecture 8 Linear algebra operations

Lecture 9 Common methods

Lecture 10 Ragged tensors

Lecture 11 Sparse tensors

Lecture 12 String tensors

Lecture 13 Variables

Section 3: [PRE-REQUISCITE] Building Neural Networks with Tensorflow

Lecture 14 Task Understanding

Lecture 15 Data Preparation

Lecture 16 Linear Regression Model

Lecture 17 Error Sanctioning

Lecture 18 Training and Optimization

Lecture 19 Performance Measurement

Lecture 20 Validation and Testing

Lecture 21 Corrective Measures

Section 4: Text Preprocessing for Sentiment Analysis

Lecture 22 Understanding Sentiment Analysis

Lecture 23 Text Standardization

Lecture 24 Tokenization

Lecture 25 One-hot encoding and Bag of Words

Lecture 26 Term frequency - Inverse Document frequency (TF-IDF)

Lecture 27 Embeddings

Section 5: Sentiment Analysis with Recurrent neural networks

Lecture 28 How Recurrent neural networks work

Lecture 29 Data Preparation

Lecture 30 Building and training RNNs

Lecture 31 Advanced RNNs (LSTM and GRU)

Lecture 32 1D Convolutional Neural Network

Section 6: Sentiment Analysis with transfer learning

Lecture 33 Understanding Word2vec

Lecture 34 Integrating pretrained Word2vec embeddings

Lecture 35 Testing

Lecture 36 Visualizing embeddings

Section 7: Neural Machine Translation with Recurrent Neural Networks

Lecture 37 Understanding Machine Translation

Lecture 38 Data Preparation

Lecture 39 Building, training and testing Model

Lecture 40 Understanding BLEU score

Lecture 41 Coding BLEU score from scratch

Section 8: Neural Machine Translation with Attention

Lecture 42 Understanding Bahdanau Attention

Lecture 43 Building, training and testing Bahdanau Attention

Section 9: Neural Machine Translation with Transformers

Lecture 44 Understanding Transformer Networks

Lecture 45 Building, training and testing Transformers

Lecture 46 Building Transformers with Custom Attention Layer

Lecture 47 Visualizing Attention scores

Section 10: Sentiment Analysis with Transformers

Lecture 48 Sentiment analysis with Transformer encoder

Lecture 49 Sentiment analysis with LSH Attention

Section 11: Transfer Learning and Generalized Language Models

Lecture 50 Understanding Transfer Learning

Lecture 51 Ulmfit

Lecture 52 Gpt

Lecture 53 Bert

Lecture 54 Albert

Lecture 55 Gpt2

Lecture 56 Roberta

Lecture 57 T5

Section 12: Sentiment Analysis with Deberta in Huggingface transformers

Lecture 58 Data Preparation

Lecture 59 Building,training and testing model

Section 13: Intent Classification with Deberta in Huggingface transformers

Lecture 60 Problem Understanding and Data Preparation

Lecture 61 Building,training and testing model

Section 14: Named Entity Relation with Roberta in Huggingface transformers

Lecture 62 Problem Understanding and Data Preparation

Lecture 63 Building,training and testing model

Section 15: Neural Machine Translation with T5 in Huggingface transformers

Lecture 64 Dataset Preparation

Lecture 65 Building,training and testing model

Section 16: Extractive Question Answering with Longformer in Huggingface transformers

Lecture 66 Problem Understanding and Data Preparation

Lecture 67 Building,training and testing model

Section 17: Ecommerce search engine with Sentence transformers

Lecture 68 Problem Understanding and Sentence Embeddings

Lecture 69 Dataset preparation

Lecture 70 Building,training and testing model

Section 18: Lyrics Generator with GPT2 in Huggingface transformers

Lecture 71 Problem Understanding and Data Preparation

Lecture 72 Building,training and testing model

Section 19: Grammatical Error Correction with T5 in Huggingface transformers

Lecture 73 Problem Understanding and Data Preparation

Lecture 74 Building,training and testing model

Section 20: Elon Musk Bot with BlenderBot in Huggingface transformers

Lecture 75 Problem Understanding and Data Preparation

Lecture 76 Building,training and testing model

Section 21: [DEPRECATED] Introduction

Lecture 77 Welcome

Lecture 78 General Introduction

Lecture 79 About this Course

Lecture 80 Link to Code

Section 22: Essential Python Programming

Lecture 81 Python Installation

Lecture 82 Variables and Basic Operators

Lecture 83 Conditional Statements

Lecture 84 Loops

Lecture 85 Methods

Lecture 86 Objects and Classes

Lecture 87 Operator Overloading

Lecture 88 Method Types

Lecture 89 Inheritance

Lecture 90 Encapsulation

Lecture 91 Polymorphism

Lecture 92 Decorators

Lecture 93 Generators

Lecture 94 Numpy Package

Lecture 95 Introduction to Matplotlib

Section 23: [DEPRECATED] Introduction to Machine Learning

Lecture 96 Task - Machine Learning Development Life Cycle

Lecture 97 Data - Machine Learning Development Life Cycle

Lecture 98 Model - Machine Learning Development Life Cycle

Lecture 99 Error Sanctioning - Machine Learning Development Life Cycle

Lecture 100 Linear Regression

Lecture 101 Logistic Regression

Lecture 102 Linear Regression Practice

Lecture 103 Logistic Regression Practice

Lecture 104 Optimization

Lecture 105 Performance Measurement

Lecture 106 Validation and Testing

Lecture 107 Softmax Regression - Data

Lecture 108 Softmax Regression - Modeling

Lecture 109 Softmax Regression - Error Sanctioning

Lecture 110 Softmax Regression - Training and Optimization

Lecture 111 Softmax Regression - Performance Measurement

Lecture 112 Neural Networks - Modeling

Lecture 113 Neural Networks - Error Sanctioning

Lecture 114 Neural Networks - Training and Optimization

Lecture 115 Training and Optimization Practice

Lecture 116 Neural Networks - Performance Measurement

Lecture 117 Neural Networks - Validation and testing

Lecture 118 Solving Overfitting and Underfitting

Lecture 119 Shuffling

Lecture 120 Ensembling

Lecture 121 Weight Initialization

Lecture 122 Data Imbalance

Lecture 123 Learning rate decay

Lecture 124 Normalization

Lecture 125 Hyperparameter tuning

Lecture 126 In Class Exercise

Section 24: [DEPRECATED] Introduction to TensorFlow 2

Lecture 127 TensorFlow Installation

Lecture 128 Introduction to TensorFlow

Lecture 129 TensorFlow Basics

Lecture 130 Training a Neural Network with TensorFlow

Section 25: [DEPRECATED] Introduction to Deep NLP with TensorFlow 2

Lecture 131 Sentiment Analysis Dataset

Lecture 132 Imdb Dataset Code

Lecture 133 Recurrent Neural Networks

Lecture 134 Training and Optimization, Evaluation

Lecture 135 Embeddings

Lecture 136 LSTM

Lecture 137 GRU

Lecture 138 1D Convolutions

Lecture 139 Bidirectional RNNs

Lecture 140 Word2Vec

Lecture 141 RNN Project

Section 26: [DEPRECATED] Neural Machine Translation with TensorFlow 2

Lecture 142 Fre-Eng Dataset and Task

Lecture 143 Sequence to Sequence Models

Lecture 144 Training Sequence to Sequence Models

Lecture 145 Performance Measurement - BLEU Score

Lecture 146 Testing Sequence to Sequence Models

Lecture 147 Attention Mechanism - Bahdanau Attention

Lecture 148 Transformers Theory

Lecture 149 Building Transformers with TensorFlow 2

Lecture 150 Text Normalization project

Section 27: Question Answering with TensorFlow 2

Lecture 151 Understanding Question Answering

Lecture 152 SQUAD dataset

Lecture 153 SQUAD dataset preparation

Lecture 154 Context - Answer Network

Lecture 155 Training and Optimization

Lecture 156 Data Augmentation

Lecture 157 LSH Attention

Lecture 158 BERT Model

Lecture 159 BERT Practice

Lecture 160 GPT Based Chatbot

Section 28: Automatic Speech Recognition

Lecture 161 What is Automatic Speech Recognition

Lecture 162 LJ- Speech Dataset

Lecture 163 Fourier Transform

Lecture 164 Short Time Fourier Transform

Lecture 165 Conv - CTC Model

Lecture 166 Speech Transformer

Lecture 167 Audio Classification project

Section 29: Image Captioning

Lecture 168 Flickr 30k Dataset

Lecture 169 CNN- Transformer Model

Lecture 170 Training and Optimization

Lecture 171 Vision Transformers

Lecture 172 OCR Project

Section 30: Shipping a Model with Google Cloud Function

Lecture 173 Introduction

Lecture 174 Model Preparation

Lecture 175 Deployment

Python Developers curious about Deep Learning for NLP,Deep Learning Practitioners who want gain a mastery of how things work under the hoods,Anyone who wants to master deep learning fundamentals and also practice deep learning for NLP using best practices in TensorFlow.,Natural Language Processing practitioners who want to learn how state of art NLP models are built and trained using deep learning.,Anyone wanting to deploy ML Models,Learners who want a practical approach to Deep learning for Natural Language Processing