Introduction :

In recent years, deep learning has become a popular approach for natural language processing (NLP) tasks. The ability of deep learning models to process large amounts of data and extract meaningful features has led to significant improvements in NLP tasks such as sentiment analysis, named entity recognition, and machine translation. This article will provide an overview of deep learning techniques applied to these tasks and discuss popular libraries and frameworks for implementing them.

Sentiment Analysis :

Sentiment analysis is determining the sentiment or emotion in a piece of text. its used for understanding customer feedback, social media posts, and other forms of text data.

import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
from textblob import TextBlob
import vaderSentiment
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer as vaderSentimentAnalyzer

nltk is a powerful python library for natural language processing, SentimentIntensityAnalyzer is a class within the nltk.sentiment module used to classify text as positive, negative, or neutral, TextBlob is a simple natural language processing library used for sentiment analysis, vaderSentiment is a lexicon-based sentiment analysis library, and SentimentIntensityAnalyzer is a class within the vaderSentiment module that used to classify text as positive, negative, or neutral.

One popular approach to sentiment analysis is to use a deep learning model such as a recurrent neural network (RNN) or a transformer.
RNNs are particularly for sentiment analysis because they can process variable length input sequences and maintain a hidden state that captures information from previous inputs. it allows them to understand the context of a sentence and make more accurate predictions.
Transformers are a newer type of deep learning model that have proven to very effective for NLP tasks such as sentiment analysis.

Named Entity Recognition :

Named entity recognition is the task of identifying and classifying named entities in text, such as people, organizations, facility, locations, etc.This can be useful for extracting structured information from unstructured text data.

import spacy
import nltk
from nltk import ne_chunk, pos_tag
from nltk.tokenize import word_tokenize

spacy is a popular and powerful library for NLP , including NER, nltk we already knows, which includes the ne_chunk() function for identifying named entities, pos_tag() for identifying the part of speech of the words in the text and word_tokenize() for tokenizing the text into words.

RNNs and transformers used for named entity recognition.
One popular approach is to use a combination of a convolutional neural network (CNN) and a bidirectional RNN.
The CNN extracts features from the input text, and the RNN processes the features and makes predictions based on the context of the text.

Machine Translation :

import torch
from torchtext import data, datasets
from torchtext.models import Transformer
import sacrebleu

torch is the Pytorch library, which provides a wide range of deep learning functionalities, torchtext is a library that provides preprocessing utilities for natural language, and Transformer is a class within the torchtext.models module used to build machine translation models. sacrebleu is a library that provides a simple and efficient way to calculate the BLEU score.

Machine translation is used to automatically translating text from one language to another language. it can be useful for breaking down language barriers and making information more accessible.

Deep learning models such as RNNs and transformers used for machine translation. we have encoder decoder architecture, where the encoder processes the input text and the decoder generates the output text.

Popular Libraries and Frameworks :

Most popular libraries are Tensorflow and Pytorch.

Tensorflow is an open source library developed by Google for building and deploying machine learning models. It has a wide range of tools for building and training deep learning models, and it is widely used in industry and research.
Sample :
```
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Embedding, LSTM, Dense
```
Pytorch is another open source library developed by Meta AI for building machine learning models.
Sample :
```
import torch
import torch.nn as nn
import torch.optim as optim
```
Both Tensorflow and Pytorch have a number of pre built models and tools for NLP tasks such as sentiment analysis, named entity recognition, and machine translation. its also provide high level APIs for building and training models, making it easy for researchers and developers to implement their own models.

Conclusion :

Deep learning has become a popular approach for natural language processing tasks such as sentiment analysis, named entity recognition, and machine translation. RNNs, transformers, and other deep learning models can be used to extract meaningful features from text data and make accurate predictions.