Joel Kelly

Joel Kelly

1591675320

Chat Bot With PyTorch - NLP And Deep Learning - Part 1

In this Python Tutorial we build a simple chatbot using PyTorch and Deep Learning. I will also provide an introduction to some basic Natural Language Processing (NLP) techniques.

  1. Theory + NLP concepts (Stemming, Tokenization, bag of words)
  2. Create training data
  3. PyTorch model and training
  4. Save/load model and implement the chat

Article “Contextual Chatbots with Tensorflow”:
https://chatbotsmagazine.com/contextual-chat-bots-with-tensorflow-4391749d0077

NLTK:
https://www.nltk.org

You can find the code on GitHub:
https://github.com/python-engineer/pytorch-chatbot

#python #chatbot #programming #deep-learning #developer

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Buddha Community

Chat Bot With PyTorch - NLP And Deep Learning - Part 1

A Wrapper for Sembast and SQFlite to Enable Easy

FHIR_DB

This is really just a wrapper around Sembast_SQFLite - so all of the heavy lifting was done by Alex Tekartik. I highly recommend that if you have any questions about working with this package that you take a look at Sembast. He's also just a super nice guy, and even answered a question for me when I was deciding which sembast version to use. As usual, ResoCoder also has a good tutorial.

I have an interest in low-resource settings and thus a specific reason to be able to store data offline. To encourage this use, there are a number of other packages I have created based around the data format FHIR. FHIR® is the registered trademark of HL7 and is used with the permission of HL7. Use of the FHIR trademark does not constitute endorsement of this product by HL7.

Using the Db

So, while not absolutely necessary, I highly recommend that you use some sort of interface class. This adds the benefit of more easily handling errors, plus if you change to a different database in the future, you don't have to change the rest of your app, just the interface.

I've used something like this in my projects:

class IFhirDb {
  IFhirDb();
  final ResourceDao resourceDao = ResourceDao();

  Future<Either<DbFailure, Resource>> save(Resource resource) async {
    Resource resultResource;
    try {
      resultResource = await resourceDao.save(resource);
    } catch (error) {
      return left(DbFailure.unableToSave(error: error.toString()));
    }
    return right(resultResource);
  }

  Future<Either<DbFailure, List<Resource>>> returnListOfSingleResourceType(
      String resourceType) async {
    List<Resource> resultList;
    try {
      resultList =
          await resourceDao.getAllSortedById(resourceType: resourceType);
    } catch (error) {
      return left(DbFailure.unableToObtainList(error: error.toString()));
    }
    return right(resultList);
  }

  Future<Either<DbFailure, List<Resource>>> searchFunction(
      String resourceType, String searchString, String reference) async {
    List<Resource> resultList;
    try {
      resultList =
          await resourceDao.searchFor(resourceType, searchString, reference);
    } catch (error) {
      return left(DbFailure.unableToObtainList(error: error.toString()));
    }
    return right(resultList);
  }
}

I like this because in case there's an i/o error or something, it won't crash your app. Then, you can call this interface in your app like the following:

final patient = Patient(
    resourceType: 'Patient',
    name: [HumanName(text: 'New Patient Name')],
    birthDate: Date(DateTime.now()),
);

final saveResult = await IFhirDb().save(patient);

This will save your newly created patient to the locally embedded database.

IMPORTANT: this database will expect that all previously created resources have an id. When you save a resource, it will check to see if that resource type has already been stored. (Each resource type is saved in it's own store in the database). It will then check if there is an ID. If there's no ID, it will create a new one for that resource (along with metadata on version number and creation time). It will save it, and return the resource. If it already has an ID, it will copy the the old version of the resource into a _history store. It will then update the metadata of the new resource and save that version into the appropriate store for that resource. If, for instance, we have a previously created patient:

{
    "resourceType": "Patient",
    "id": "fhirfli-294057507-6811107",
    "meta": {
        "versionId": "1",
        "lastUpdated": "2020-10-16T19:41:28.054369Z"
    },
    "name": [
        {
            "given": ["New"],
            "family": "Patient"
        }
    ],
    "birthDate": "2020-10-16"
}

And we update the last name to 'Provider'. The above version of the patient will be kept in _history, while in the 'Patient' store in the db, we will have the updated version:

{
    "resourceType": "Patient",
    "id": "fhirfli-294057507-6811107",
    "meta": {
        "versionId": "2",
        "lastUpdated": "2020-10-16T19:45:07.316698Z"
    },
    "name": [
        {
            "given": ["New"],
            "family": "Provider"
        }
    ],
    "birthDate": "2020-10-16"
}

This way we can keep track of all previous version of all resources (which is obviously important in medicine).

For most of the interactions (saving, deleting, etc), they work the way you'd expect. The only difference is search. Because Sembast is NoSQL, we can search on any of the fields in a resource. If in our interface class, we have the following function:

  Future<Either<DbFailure, List<Resource>>> searchFunction(
      String resourceType, String searchString, String reference) async {
    List<Resource> resultList;
    try {
      resultList =
          await resourceDao.searchFor(resourceType, searchString, reference);
    } catch (error) {
      return left(DbFailure.unableToObtainList(error: error.toString()));
    }
    return right(resultList);
  }

You can search for all immunizations of a certain patient:

searchFunction(
        'Immunization', 'patient.reference', 'Patient/$patientId');

This function will search through all entries in the 'Immunization' store. It will look at all 'patient.reference' fields, and return any that match 'Patient/$patientId'.

The last thing I'll mention is that this is a password protected db, using AES-256 encryption (although it can also use Salsa20). Anytime you use the db, you have the option of using a password for encryption/decryption. Remember, if you setup the database using encryption, you will only be able to access it using that same password. When you're ready to change the password, you will need to call the update password function. If we again assume we created a change password method in our interface, it might look something like this:

class IFhirDb {
  IFhirDb();
  final ResourceDao resourceDao = ResourceDao();
  ...
    Future<Either<DbFailure, Unit>> updatePassword(String oldPassword, String newPassword) async {
    try {
      await resourceDao.updatePw(oldPassword, newPassword);
    } catch (error) {
      return left(DbFailure.unableToUpdatePassword(error: error.toString()));
    }
    return right(Unit);
  }

You don't have to use a password, and in that case, it will save the db file as plain text. If you want to add a password later, it will encrypt it at that time.

General Store

After using this for a while in an app, I've realized that it needs to be able to store data apart from just FHIR resources, at least on occasion. For this, I've added a second class for all versions of the database called GeneralDao. This is similar to the ResourceDao, but fewer options. So, in order to save something, it would look like this:

await GeneralDao().save('password', {'new':'map'});
await GeneralDao().save('password', {'new':'map'}, 'key');

The difference between these two options is that the first one will generate a key for the map being stored, while the second will store the map using the key provided. Both will return the key after successfully storing the map.

Other functions available include:

// deletes everything in the general store
await GeneralDao().deleteAllGeneral('password'); 

// delete specific entry
await GeneralDao().delete('password','key'); 

// returns map with that key
await GeneralDao().find('password', 'key'); 

FHIR® is a registered trademark of Health Level Seven International (HL7) and its use does not constitute an endorsement of products by HL7®

Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add fhir_db

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

dependencies:
  fhir_db: ^0.4.3

Alternatively, your editor might support or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:fhir_db/dstu2.dart';
import 'package:fhir_db/dstu2/fhir_db.dart';
import 'package:fhir_db/dstu2/general_dao.dart';
import 'package:fhir_db/dstu2/resource_dao.dart';
import 'package:fhir_db/encrypt/aes.dart';
import 'package:fhir_db/encrypt/salsa.dart';
import 'package:fhir_db/r4.dart';
import 'package:fhir_db/r4/fhir_db.dart';
import 'package:fhir_db/r4/general_dao.dart';
import 'package:fhir_db/r4/resource_dao.dart';
import 'package:fhir_db/r5.dart';
import 'package:fhir_db/r5/fhir_db.dart';
import 'package:fhir_db/r5/general_dao.dart';
import 'package:fhir_db/r5/resource_dao.dart';
import 'package:fhir_db/stu3.dart';
import 'package:fhir_db/stu3/fhir_db.dart';
import 'package:fhir_db/stu3/general_dao.dart';
import 'package:fhir_db/stu3/resource_dao.dart'; 

example/lib/main.dart

import 'package:fhir/r4.dart';
import 'package:fhir_db/r4.dart';
import 'package:flutter/material.dart';
import 'package:test/test.dart';

Future<void> main() async {
  WidgetsFlutterBinding.ensureInitialized();

  final resourceDao = ResourceDao();

  // await resourceDao.updatePw('newPw', null);
  await resourceDao.deleteAllResources(null);

  group('Playing with passwords', () {
    test('Playing with Passwords', () async {
      final patient = Patient(id: Id('1'));

      final saved = await resourceDao.save(null, patient);

      await resourceDao.updatePw(null, 'newPw');
      final search1 = await resourceDao.find('newPw',
          resourceType: R4ResourceType.Patient, id: Id('1'));
      expect(saved, search1[0]);

      await resourceDao.updatePw('newPw', 'newerPw');
      final search2 = await resourceDao.find('newerPw',
          resourceType: R4ResourceType.Patient, id: Id('1'));
      expect(saved, search2[0]);

      await resourceDao.updatePw('newerPw', null);
      final search3 = await resourceDao.find(null,
          resourceType: R4ResourceType.Patient, id: Id('1'));
      expect(saved, search3[0]);

      await resourceDao.deleteAllResources(null);
    });
  });

  final id = Id('12345');
  group('Saving Things:', () {
    test('Save Patient', () async {
      final humanName = HumanName(family: 'Atreides', given: ['Duke']);
      final patient = Patient(id: id, name: [humanName]);
      final saved = await resourceDao.save(null, patient);

      expect(saved.id, id);

      expect((saved as Patient).name?[0], humanName);
    });

    test('Save Organization', () async {
      final organization = Organization(id: id, name: 'FhirFli');
      final saved = await resourceDao.save(null, organization);

      expect(saved.id, id);

      expect((saved as Organization).name, 'FhirFli');
    });

    test('Save Observation1', () async {
      final observation1 = Observation(
        id: Id('obs1'),
        code: CodeableConcept(text: 'Observation #1'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save(null, observation1);

      expect(saved.id, Id('obs1'));

      expect((saved as Observation).code.text, 'Observation #1');
    });

    test('Save Observation1 Again', () async {
      final observation1 = Observation(
          id: Id('obs1'),
          code: CodeableConcept(text: 'Observation #1 - Updated'));
      final saved = await resourceDao.save(null, observation1);

      expect(saved.id, Id('obs1'));

      expect((saved as Observation).code.text, 'Observation #1 - Updated');

      expect(saved.meta?.versionId, Id('2'));
    });

    test('Save Observation2', () async {
      final observation2 = Observation(
        id: Id('obs2'),
        code: CodeableConcept(text: 'Observation #2'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save(null, observation2);

      expect(saved.id, Id('obs2'));

      expect((saved as Observation).code.text, 'Observation #2');
    });

    test('Save Observation3', () async {
      final observation3 = Observation(
        id: Id('obs3'),
        code: CodeableConcept(text: 'Observation #3'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save(null, observation3);

      expect(saved.id, Id('obs3'));

      expect((saved as Observation).code.text, 'Observation #3');
    });
  });

  group('Finding Things:', () {
    test('Find 1st Patient', () async {
      final search = await resourceDao.find(null,
          resourceType: R4ResourceType.Patient, id: id);
      final humanName = HumanName(family: 'Atreides', given: ['Duke']);

      expect(search.length, 1);

      expect((search[0] as Patient).name?[0], humanName);
    });

    test('Find 3rd Observation', () async {
      final search = await resourceDao.find(null,
          resourceType: R4ResourceType.Observation, id: Id('obs3'));

      expect(search.length, 1);

      expect(search[0].id, Id('obs3'));

      expect((search[0] as Observation).code.text, 'Observation #3');
    });

    test('Find All Observations', () async {
      final search = await resourceDao.getResourceType(
        null,
        resourceTypes: [R4ResourceType.Observation],
      );

      expect(search.length, 3);

      final idList = [];
      for (final obs in search) {
        idList.add(obs.id.toString());
      }

      expect(idList.contains('obs1'), true);

      expect(idList.contains('obs2'), true);

      expect(idList.contains('obs3'), true);
    });

    test('Find All (non-historical) Resources', () async {
      final search = await resourceDao.getAll(null);

      expect(search.length, 5);
      final patList = search.toList();
      final orgList = search.toList();
      final obsList = search.toList();
      patList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Patient);
      orgList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Organization);
      obsList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Observation);

      expect(patList.length, 1);

      expect(orgList.length, 1);

      expect(obsList.length, 3);
    });
  });

  group('Deleting Things:', () {
    test('Delete 2nd Observation', () async {
      await resourceDao.delete(
          null, null, R4ResourceType.Observation, Id('obs2'), null, null);

      final search = await resourceDao.getResourceType(
        null,
        resourceTypes: [R4ResourceType.Observation],
      );

      expect(search.length, 2);

      final idList = [];
      for (final obs in search) {
        idList.add(obs.id.toString());
      }

      expect(idList.contains('obs1'), true);

      expect(idList.contains('obs2'), false);

      expect(idList.contains('obs3'), true);
    });

    test('Delete All Observations', () async {
      await resourceDao.deleteSingleType(null,
          resourceType: R4ResourceType.Observation);

      final search = await resourceDao.getAll(null);

      expect(search.length, 2);

      final patList = search.toList();
      final orgList = search.toList();
      patList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Patient);
      orgList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Organization);

      expect(patList.length, 1);

      expect(patList.length, 1);
    });

    test('Delete All Resources', () async {
      await resourceDao.deleteAllResources(null);

      final search = await resourceDao.getAll(null);

      expect(search.length, 0);
    });
  });

  group('Password - Saving Things:', () {
    test('Save Patient', () async {
      await resourceDao.updatePw(null, 'newPw');
      final humanName = HumanName(family: 'Atreides', given: ['Duke']);
      final patient = Patient(id: id, name: [humanName]);
      final saved = await resourceDao.save('newPw', patient);

      expect(saved.id, id);

      expect((saved as Patient).name?[0], humanName);
    });

    test('Save Organization', () async {
      final organization = Organization(id: id, name: 'FhirFli');
      final saved = await resourceDao.save('newPw', organization);

      expect(saved.id, id);

      expect((saved as Organization).name, 'FhirFli');
    });

    test('Save Observation1', () async {
      final observation1 = Observation(
        id: Id('obs1'),
        code: CodeableConcept(text: 'Observation #1'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save('newPw', observation1);

      expect(saved.id, Id('obs1'));

      expect((saved as Observation).code.text, 'Observation #1');
    });

    test('Save Observation1 Again', () async {
      final observation1 = Observation(
          id: Id('obs1'),
          code: CodeableConcept(text: 'Observation #1 - Updated'));
      final saved = await resourceDao.save('newPw', observation1);

      expect(saved.id, Id('obs1'));

      expect((saved as Observation).code.text, 'Observation #1 - Updated');

      expect(saved.meta?.versionId, Id('2'));
    });

    test('Save Observation2', () async {
      final observation2 = Observation(
        id: Id('obs2'),
        code: CodeableConcept(text: 'Observation #2'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save('newPw', observation2);

      expect(saved.id, Id('obs2'));

      expect((saved as Observation).code.text, 'Observation #2');
    });

    test('Save Observation3', () async {
      final observation3 = Observation(
        id: Id('obs3'),
        code: CodeableConcept(text: 'Observation #3'),
        effectiveDateTime: FhirDateTime(DateTime(1981, 09, 18)),
      );
      final saved = await resourceDao.save('newPw', observation3);

      expect(saved.id, Id('obs3'));

      expect((saved as Observation).code.text, 'Observation #3');
    });
  });

  group('Password - Finding Things:', () {
    test('Find 1st Patient', () async {
      final search = await resourceDao.find('newPw',
          resourceType: R4ResourceType.Patient, id: id);
      final humanName = HumanName(family: 'Atreides', given: ['Duke']);

      expect(search.length, 1);

      expect((search[0] as Patient).name?[0], humanName);
    });

    test('Find 3rd Observation', () async {
      final search = await resourceDao.find('newPw',
          resourceType: R4ResourceType.Observation, id: Id('obs3'));

      expect(search.length, 1);

      expect(search[0].id, Id('obs3'));

      expect((search[0] as Observation).code.text, 'Observation #3');
    });

    test('Find All Observations', () async {
      final search = await resourceDao.getResourceType(
        'newPw',
        resourceTypes: [R4ResourceType.Observation],
      );

      expect(search.length, 3);

      final idList = [];
      for (final obs in search) {
        idList.add(obs.id.toString());
      }

      expect(idList.contains('obs1'), true);

      expect(idList.contains('obs2'), true);

      expect(idList.contains('obs3'), true);
    });

    test('Find All (non-historical) Resources', () async {
      final search = await resourceDao.getAll('newPw');

      expect(search.length, 5);
      final patList = search.toList();
      final orgList = search.toList();
      final obsList = search.toList();
      patList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Patient);
      orgList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Organization);
      obsList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Observation);

      expect(patList.length, 1);

      expect(orgList.length, 1);

      expect(obsList.length, 3);
    });
  });

  group('Password - Deleting Things:', () {
    test('Delete 2nd Observation', () async {
      await resourceDao.delete(
          'newPw', null, R4ResourceType.Observation, Id('obs2'), null, null);

      final search = await resourceDao.getResourceType(
        'newPw',
        resourceTypes: [R4ResourceType.Observation],
      );

      expect(search.length, 2);

      final idList = [];
      for (final obs in search) {
        idList.add(obs.id.toString());
      }

      expect(idList.contains('obs1'), true);

      expect(idList.contains('obs2'), false);

      expect(idList.contains('obs3'), true);
    });

    test('Delete All Observations', () async {
      await resourceDao.deleteSingleType('newPw',
          resourceType: R4ResourceType.Observation);

      final search = await resourceDao.getAll('newPw');

      expect(search.length, 2);

      final patList = search.toList();
      final orgList = search.toList();
      patList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Patient);
      orgList.retainWhere(
          (resource) => resource.resourceType == R4ResourceType.Organization);

      expect(patList.length, 1);

      expect(patList.length, 1);
    });

    test('Delete All Resources', () async {
      await resourceDao.deleteAllResources('newPw');

      final search = await resourceDao.getAll('newPw');

      expect(search.length, 0);

      await resourceDao.updatePw('newPw', null);
    });
  });
} 

Download Details:

Author: MayJuun

Source Code: https://github.com/MayJuun/fhir/tree/main/fhir_db

#sqflite  #dart  #flutter 

Margaret D

Margaret D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

PyTorch For Deep Learning 

What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

Prerequisites

Python, Numpy, Pandas and Matplotlib

Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

Vaughn  Sauer

Vaughn Sauer

1620755822

Deep Learning Vs NLP: Difference Between Deep Learning & NLP

When we think of Artificial Intelligence, it becomes almost overwhelming to wrap our brains around complex terms like Machine Learning, Deep Learning, and Natural Language Processing (NLP). After all, these new-age disciplines are much more advanced and intricate than anything we’ve ever seen. This is primarily why people tend to use AI terminologies synonymously, sparking a debate of sorts between different concepts of Data Science.

One such trending debate is that of Deep Learning vs. NLP. While Deep Learning and NLP fall under the broad umbrella of Artificial Intelligence, the difference between Deep Learning and NLP is pretty stark!

In this post, we’ll take a detailed look into the Deep Learning vs. NLP debate, understand their importance in the AI domain, see how they associate with one another, and learn about the differences between Deep Learning and NLP.

So, without further ado, let’s get straight into it!

Deep Learning vs. NLP

What is Deep Learning?

Deep Learning is a branch of Machine Learning that leverages artificial neural networks (ANNs)to simulate the human brain’s functioning. An artificial neural network is made of an interconnected web of thousands or millions of neurons stacked in multiple layers, hence the name Deep Learning.

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