Modern Natural Language Processing(Nlp) Using Deep Learning.

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Linear Regression, Logistic Regression and Neural Networks built from scratch.
TensorFlow installation, Basics and training neural networks with TensorFlow 2.
Convolutional Neural Networks, Modern ConvNets, training object recognition models with TensorFlow 2.
Recurrent Neural Networks, Modern RNNs, training sennt analysis models with TensorFlow 2.
Neural Machine Translation, Question Answering, Image Captioning, Sennt Analysis, Speech recognition
Deploying a Deep Learning Model with Google Cloud Function.
Requirements
Basic Math
No Programming experience.
Description
In this course, we shall look at core Deep Learning concepts and apply our knowledge to solve real world problems in Natural Language Processing using the Python Programming Language and TensorFlow 2. We shall explain core Machine Learning topics like Linear Regression, Logistic Regression, Multi-class classification and Neural Networks. If you've gotten to this point, it means you are interested in mastering Deep Learning For NLP and using your skills to solve practical problems.You may already have some knowledge on Machine learning, Natural Language Processing or Deep Learning, or you may be coming in contact with Deep Learning for the very first . It doesn't matter from which end you come from, because at the end of this course, you shall be an expert with much hands-on experience.You shall work on several projects like Sennt Analysis, Machine Translation, Question Answering, Image captioning, speech recognition and more, using knowledge gained from this course.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 .Here are the different concepts you'll master after completing this course.Fundamentals Machine Learning.Essential Python ProgrammingChoosing Machine Model based on taskError sanctioningLinear RegressionLogistic RegressionMulti-class RegressionNeural NetworksTraining and optimizationPerformance MeasurementValidation and TestingBuilding Machine Learning models from scratch in python.Overfitting and UnderfittingShufflingEnsemblingWeight initializationData imbalanceLearning rate decayNormalizationHyperparameter tuningTensorFlow InstallationTraining neural networks with TensorFlow 2Imagenet training with TensorFlowConvolutional Neural NetworksVGGNetsResNetsInceptionNetsMobileNetsEfficientNetsTransfer Learning and FineTuningData AugmentationCallbacksMonitoring with TensorboardIMDB Dataset Sennt AnalysisRecurrent Neural Networks.LSU1D ConvolutionBi directional RNNWord2VecMachine TranslationAttention ModelTransformer NetworkVision TransformersLSH AttentionImage CaptioningQuestion AnsweringBERT ModelHuggingFaceDeploying A Deep Learning Model with Google Cloud FunctionsYOU'LL ALSO GET:Life access to This CourseFriendly and Prompt support in the Q&A sectionUdemy Certificate of Completion available for 30-day money back guaranteeWho this course is for:Bner Python Developers curious about Applying Deep Learning for NLPNLP practitioners who want to learn how state of art Natural Language Processing models are built and trained using deep learning.Anyone who wants to master deep learning fundamentals and also practice deep learning for NLP using best practices in TensorFlow 2.Deep Learning for NLP Practitioners who want gain a mastery of how things work under the hood.Enjoy!!!

Overview

Section 1: Introduction

Lecture 1 Welcome

Lecture 2 General Introduction

Lecture 3 About this Course

Section 2: Essential Python Programming

Lecture 4 Python Installation

Lecture 5 Variables and Basic Operators

Lecture 6 Conditional Statements

Lecture 7 Loops

Lecture 8 Methods

Lecture 9 Objects and Classes

Lecture 10 Operator Overloading

Lecture 11 Method Types

Lecture 12 Inheritance

Lecture 13 Encapsulation

Lecture 14 Polymorphism

Lecture 15 Decorators

Lecture 16 Generators

Lecture 17 Numpy Package

Lecture 18 Introduction to Matplotlib

Section 3: Introduction to Machine Learning

Lecture 19 Task - Machine Learning Development Life Cycle

Lecture 20 Data - Machine Learning Development Life Cycle

Lecture 21 Model - Machine Learning Development Life Cycle

Lecture 22 Error Sanctioning - Machine Learning Development Life Cycle

Lecture 23 Linear Regression

Lecture 24 Logistic Regression

Lecture 25 Linear Regression Practice

Lecture 26 Logistic Regression Practice

Lecture 27 Optimization

Lecture 28 Performance Measurement

Lecture 29 Validation and Testing

Lecture 30 Softmax Regression - Data

Lecture 31 Softmax Regression - Modeling

Lecture 32 Softmax Regression - Error Sanctioning

Lecture 33 Softmax Regression - Training and Optimization

Lecture 34 Softmax Regression - Performance Measurement

Lecture 35 Neural Networks - Modeling

Lecture 36 Neural Networks - Error Sanctioning

Lecture 37 Neural Networks - Training and Optimization

Lecture 38 Training and Optimization Practice

Lecture 39 Neural Networks - Performance Measurement

Lecture 40 Neural Networks - Validation and testing

Lecture 41 Solving Overfitting and Underfitting

Lecture 42 Shuffling

Lecture 43 Ensembling

Lecture 44 Weight Initialization

Lecture 45 Data Imbalance

Lecture 46 Learning rate decay

Lecture 47 Normalization

Lecture 48 Hyperparameter tuning

Lecture 49 In Class Exercise

Section 4: Introduction to TensorFlow 2

Lecture 50 TensorFlow Installation

Lecture 51 Introduction to TensorFlow

Lecture 52 TensorFlow Basics

Lecture 53 Training a Neural Network with TensorFlow

Section 5: Introduction to Deep NLP with TensorFlow 2

Lecture 54 Sennt Analysis Dataset

Lecture 55 Imdb Dataset Code

Lecture 56 Recurrent Neural Networks

Lecture 57 Training and Optimization, Evaluation

Lecture 58 Embeddings

Lecture 59 LSTM

Lecture 60 GRU

Lecture 61 1D Convolutions

Lecture 62 Bidirectional RNNs

Lecture 63 Word2Vec

Lecture 64 Word2Vec Practice

Lecture 65 RNN Project

Section 6: Neural Machine Translation with TensorFlow 2

Lecture 66 Fre-Eng Dataset and Task

Lecture 67 Sequence to Sequence Models

Lecture 68 Training Sequence to Sequence Models

Lecture 69 Performance Measurement - BLEU Score

Lecture 70 Testing Sequence to Sequence Models

Lecture 71 Attention Mechanism - Bahdanau Attention

Lecture 72 Transformers Theory

Lecture 73 Building Transformers with TensorFlow 2

Lecture 74 Text Normalization project

Section 7: Question Answering with TensorFlow 2

Lecture 75 Understanding Question Answering

Lecture 76 SQUAD dataset

Lecture 77 SQUAD dataset preparation

Lecture 78 Context - Answer Network

Lecture 79 Training and Optimization

Lecture 80 Data Augmentation

Lecture 81 LSH Attention

Lecture 82 BERT Model

Lecture 83 BERT Practice

Lecture 84 GPT Based Chatbot

Section 8: Automatic Speech Recognition

Lecture 85 What is Automatic Speech Recognition

Lecture 86 LJ- Speech Dataset

Lecture 87 Fourier Transform

Lecture 88 Short Fourier Transform

Lecture 89 Conv - CTC Model

Lecture 90 Speech Transformer

Lecture 91 Audio Classification project

Section 9: Image Captioning

Lecture 92 Flickr 30k Dataset

Lecture 93 CNN- Transformer Model

Lecture 94 Training and Optimization

Lecture 95 Vision Transformers

Lecture 96 OCR Project

Section 10: Shipping a Model with Google Cloud Function

Lecture 97 Introduction

Lecture 98 Model Preparation

Lecture 99 Deployment

Bner Python Developers curious about Deep Learning.,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 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.

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