Download Modern Natural Language Processing(NLP) using Deep Learning.

What you would learn in Modern Natural Language Processing(NLP) using Deep Learning course?

This course will examine the fundamental Deep Learning concepts and apply our understanding to solve real-world issues within Natural Language Processing using the Python Programming Language and TensorFlow 2. We will discuss the most fundamental Machine Learning topics like Linear Regression, Logistic Regression, Multi-class classification, and Neural Networks. If you've reached this point, that means you're interested in learning Deep Learning For NLP and using your skills to tackle real-world issues.

Perhaps you already have experience with Machine learning, Natural Language Processing, or Deep Learning, or you might be getting acquainted with the concept of Deep Learning for the first time. It doesn't matter which side you're from since at the end of the course you'll be an expert, with a lot of practical knowledge.

Using the skills you've learned through this course, you will be working on various projects such as Sentiment Analysis, Machine Translation, Question Answering, Image captioning, Speech Recognition, and many other things.

If you're ready to leap to your current career goals, this program is for you, and we're thrilled to assist you in achieving your objectives!

This course is made available via Neuralearn. Like every other course offered by Neuralearn, We place great importance on feedback. Your comments and questions on the forum will assist us in improving this course. Please feel free to post any questions you want to ask in the forum. We try our best to answer within the shortest time possible.

These are the key concepts you'll be able to master after completing this course.

• Fundamentals Machine Learning.

• Essential Python Programming

• The selection of a machine model is Based on the task

• Error sanctioning

• Linear Regression

• Logistic Regression

• Multi-class Regression

• Neural Networks

• Training and optimization

• Performance Evaluation

• Validation and Testing

• Designing Machine Learning models by hand in Python.

• Overfitting and underfitting

• Shuffling

• Ensembling

• Weight initialization

• Data imbalance

• Learning rate decay

• Normalization

• Hyperparameter tuning

• TensorFlow Installation

• Training neural networks by using TensorFlow 2.

• Imagenet training using TensorFlow

• Convolutional Neural Networks

• VGGNets

• ResNets

• InceptionNets

• MobileNets

• EfficientNets

• Transfer Learning and FineTuning

• Data Augmentation

• Callbacks

• Monitoring using 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

• Answering Questions

• BERT Model

• HuggingFace

• Implementing a Deep Learning Model with Google Cloud Functions

Content of the Course

• An introduction to Python and more advanced concepts such as Object-Oriented Programming, decorators generators, and specific libraries such as Numpy and Matplotlib
• Understanding the fundamental concepts in Machine Learning and The Machine Learning Development Lifecycle.
• Linear Regression, Logistic Regression, and Neural Networks built from scratch.
• TensorFlow installation, basics, and learning neural networks using TensorFlow 2.
• Convolutional Neural Networks, Modern ConvNets for training object recognition models using TensorFlow 2.
• Recurrent Neural Networks, Modern RNNs, and training sentiment analysis models that use TensorFlow 2.
• Neural Machine Translation Question Answering image captioning, sentiment analysis Speech Recognition
• Implementing the Deep Learning Model with Google Cloud Function.

Download Modern Natural Language Processing(NLP) using Deep Learning from below links NOW!