Modern Natural Language Processing(Nlp) Using Deep Learning by Neuralearn Dot AI

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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.

Homepage

 https://www.udemy.com/course/modern-natural-language-processingnlp-using-deep-learning/ 

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