Modern Natural Language Processing(NLP) using Deep Learning.

Implement Sentiment Analysis, Speech Recognition, Translation, Question Answering & Question Answering with TensorFlow 2

4.63 (16 reviews)
Udemy
platform
English
language
Data Science
category
69
students
28 hours
content
Jul 2022
last update
$84.99
regular price

What you will learn

Introductory Python, to more advanced concepts like Object Oriented Programming, decorators, generators, and even specialized libraries like Numpy & Matplotlib

Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle.

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 sentiment analysis models with TensorFlow 2.

Neural Machine Translation, Question Answering, Image Captioning, Sentiment Analysis, Speech recognition

Deploying a Deep Learning Model with Google Cloud Function.

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 time. 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 Sentiment 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 time.


Here are the different concepts you'll master after completing this course.

  • Fundamentals Machine Learning.

  • Essential Python Programming

  • Choosing Machine Model based on task

  • Error sanctioning

  • Linear Regression

  • Logistic Regression

  • Multi-class Regression

  • Neural Networks

  • Training and optimization

  • Performance Measurement

  • Validation and Testing

  • Building Machine Learning models from scratch in python.

  • Overfitting and Underfitting

  • Shuffling

  • Ensembling

  • Weight initialization

  • Data imbalance

  • Learning rate decay

  • Normalization

  • Hyperparameter tuning

  • TensorFlow Installation

  • Training neural networks with TensorFlow 2

  • Imagenet training with TensorFlow

  • Convolutional Neural Networks

  • VGGNets

  • ResNets

  • InceptionNets

  • MobileNets

  • EfficientNets

  • Transfer Learning and FineTuning

  • Data Augmentation

  • Callbacks

  • Monitoring with Tensorboard

  • IMDB Dataset

  • Sentiment Analysis

  • Recurrent Neural Networks.

  • LSTM

  • GRU

  • 1D Convolution

  • Bi directional RNN

  • Word2Vec

  • Machine Translation

  • Attention Model

  • Transformer Network

  • Vision Transformers

  • LSH Attention

  • Image Captioning

  • Question Answering

  • BERT Model

  • HuggingFace

  • Deploying A Deep Learning Model with Google Cloud Functions

YOU'LL ALSO GET:

  • Lifetime access to This Course

  • Friendly and Prompt support in the Q&A section

  • Udemy Certificate of Completion available for download

  • 30-day money back guarantee

Who this course is for:

  • Beginner Python Developers curious about Applying Deep Learning for NLP

  • NLP 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!!!

Screenshots

Modern Natural Language Processing(NLP) using Deep Learning. - Screenshot_01Modern Natural Language Processing(NLP) using Deep Learning. - Screenshot_02Modern Natural Language Processing(NLP) using Deep Learning. - Screenshot_03Modern Natural Language Processing(NLP) using Deep Learning. - Screenshot_04

Content

Introduction

Welcome
General Introduction
About this Course

Essential Python Programming

Python Installation
Variables and Basic Operators
Conditional Statements
Loops
Methods
Objects and Classes
Operator Overloading
Method Types
Inheritance
Encapsulation
Polymorphism
Decorators
Generators
Numpy Package
Introduction to Matplotlib

Introduction to Machine Learning

Task - Machine Learning Development Life Cycle
Data - Machine Learning Development Life Cycle
Model - Machine Learning Development Life Cycle
Error Sanctioning - Machine Learning Development Life Cycle
Linear Regression
Logistic Regression
Linear Regression Practice
Logistic Regression Practice
Optimization
Performance Measurement
Validation and Testing
Softmax Regression - Data
Softmax Regression - Modeling
Softmax Regression - Error Sanctioning
Softmax Regression - Training and Optimization
Softmax Regression - Performance Measurement
Neural Networks - Modeling
Neural Networks - Error Sanctioning
Neural Networks - Training and Optimization
Training and Optimization Practice
Neural Networks - Performance Measurement
Neural Networks - Validation and testing
Solving Overfitting and Underfitting
Shuffling
Ensembling
Weight Initialization
Data Imbalance
Learning rate decay
Normalization
Hyperparameter tuning
In Class Exercise

Introduction to TensorFlow 2

TensorFlow Installation
Introduction to TensorFlow
TensorFlow Basics
Training a Neural Network with TensorFlow

Introduction to Deep NLP with TensorFlow 2

Sentiment Analysis Dataset
Imdb Dataset Code
Recurrent Neural Networks
Training and Optimization, Evaluation
Embeddings
LSTM
GRU
1D Convolutions
Bidirectional RNNs
Word2Vec
RNN Project

Neural Machine Translation with TensorFlow 2

Fre-Eng Dataset and Task
Sequence to Sequence Models
Training Sequence to Sequence Models
Performance Measurement - BLEU Score
Testing Sequence to Sequence Models
Attention Mechanism - Bahdanau Attention
Transformers Theory
Building Transformers with TensorFlow 2
Text Normalization project

Question Answering with TensorFlow 2

Understanding Question Answering
SQUAD dataset
SQUAD dataset preparation
Context - Answer Network
Training and Optimization
Data Augmentation
LSH Attention
BERT Model
BERT Practice
GPT Based Chatbot

Automatic Speech Recognition

What is Automatic Speech Recognition
LJ- Speech Dataset
Fourier Transform
Short Time Fourier Transform
Conv - CTC Model
Speech Transformer
Audio Classification project

Image Captioning

Flickr 30k Dataset
CNN- Transformer Model
Training and Optimization
Vision Transformers
OCR Project

Shipping a Model with Google Cloud Function

Introduction
Model Preparation
Deployment
4154714
udemy ID
6/30/2021
course created date
6/29/2022
course indexed date
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