Deep Learning: NLP for Sentiment analysis & Translation 2023

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Neuralearn Dot AI

20:36:16

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1 - Introduction

1 - Welcome.mp4

02:58

2 - General intro.mp4

20:29

2 - Tensors and variables

3 - Basics.mp4

08:06

4 - Initialization and Casting.mp4

01:03:40

5 - Indexing.mp4

14:28

6 - Maths Operations.mp4

32:49

7 - Linear algebra operations.mp4

01:01:16

8 - Common methods.mp4

53:50

9 - Ragged tensors.mp4

11:20

10 - Sparse tensors.mp4

02:41

11 - String tensors.mp4

03:21

12 - Variables.mp4

07:05

3 - PREREQUISCITE Building neural networks with tensorflow

13 - Task understanding.mp4

04:51

14 - Data preparation.mp4

34:56

15 - Linear regression model.mp4

15:30

16 - Error sanctioning.mp4

14:32

17 - Training and optimization.mp4

16:27

18 - Performance measurement.mp4

02:53

19 - Validation and testing.mp4

20:09

20 - Corrective measures.mp4

24:19

4 - Text Preprocessing for Sentiment Analysis

21 - Understanding Sentiment Analysis.mp4

10:41

22 - Text Standardization.mp4

14:37

23 - Tokenization.mp4

17:55

24 - Onehot encoding and Bag of Words.mp4

15:07

25 - Term frequency Inverse Document frequency TFIDF.mp4

09:51

26 - Embeddings.mp4

11:38

5 - Sentiment Analysis with Recurrent neural networks

27 - How Recurrent neural networks work.mp4

21:40

28 - Data preparation.mp4

26:43

29 - Building and training RNNs.mp4

23:17

30 - Advanced RNNs LSTM and GRU.mp4

34:19

31 - 1D Convolutional Neural Network.mp4

30:36

6 - Sentiment Analysis with transfer learning

32 - Understanding Word2vec.mp4

26:04

33 - Integrating pretrained Word2vec embeddings.mp4

12:56

34 - Testing.mp4

07:07

35 - Visualizing embeddings.mp4

15:33

7 - Neural Machine Translation with Recurrent Neural Networks

36 - Understanding Machine Translation.mp4

13:23

37 - Data Preparation.mp4

26:41

38 - Building training and testing Model.mp4

29:38

39 - Understanding BLEU score.mp4

10:58

40 - Coding BLEU score from scratch.mp4

14:17

8 - Neural Machine Translation with Attention

41 - Understanding Bahdanau Attention.mp4

20:24

42 - Building training and testing Bahdanau Attention.mp4

21:14

9 - Neural Machine Translation with Transformers

43 - Understanding Transformer Networks.mp4

01:02:36

44 - Building training and testing Transformers.mp4

55:53

45 - Building Transformers with Custom Attention Layer.mp4

18:30

46 - Visualizing Attention scores.mp4

12:12

10 - Sentiment Analysis with Transformers

47 - Sentiment analysis with Transformer encoder.mp4

07:52

48 - Sentiment analysis with LSH Attention.mp4

01:11:35

11 - Transfer Learning and Generalized Language Models

49 - Understanding Transfer Learning.mp4

12:22

50 - Ulmfit.mp4

04:39

51 - Gpt.mp4

20:06

52 - Bert.mp4

18:28

53 - Albert.mp4

05:13

54 - Gpt2.mp4

11:56

55 - Roberta.mp4

02:04

56 - T5.mp4

08:55

12 - Sentiment Analysis with Deberta in Huggingface transformers

57 - Data Preparation.mp4

45:13

58 - Buildingtraining and testing model.mp4

26:20

13 - Neural Machine Translation with T5 in Huggingface transformers

59 - Dataset Preparation.mp4

13:32

60 - Buildingtraining and testing model.mp4

08:31

Description

Master and Deploy Sentiment analysis and machine translation solutions with Tensorflow and Hugggingface Transformers

What You'll Learn?

The Basics of Tensors and Variables with Tensorflow
Linear Regression, Logistic Regression and Neural Networks built from scratch.
Basics of Tensorflow and training neural networks with TensorFlow 2.
Model deployment
Conversion from tensorflow to Onnx Model
Quantization Aware training
Building API with Fastapi
Deploying API to the Cloud
Sentiment Analysis with Recurrent neural networks, Attention Models and Transformers from scratch
Neural Machine Translation with Recurrent neural networks, Attention Models and Transformers from scratch
Neural Machine Translation with T5 in Huggingface transformers
Attention Networks
Transformers from scratch

Who is this for?

Beginner Python Developers curious about Applying Deep Learning for Natural Language Processing in the domains of sentiment analysis and machine translation

Deep Learning for NLP Practitioners who want gain a mastery of how things work under the hood

NLP practitioners who want to learn how state of art sentiment analysis and machine translation models are built and trained using deep learning.

Anyone wanting to deploy ML Models

Learners who want a practical approach to Deep learning for Sentiment analysis and Machine Translation

More details

Description
Sentiment analysis and machine translation models are used by millions of people every single day. These deep learning models (most notably transformers) power different industries today.
With the creation of much more efficient deep learning models, from the early 2010s, we have seen a great improvement in the state of the art in the domains of sentiment analysis and machine translation.
In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how to process text in the context of natural language processing, then we would dive into building our own models and deploying them to the cloud while observing best practices.
We are going to be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and Huggingface

You will learn:
The Basics of Tensorflow (Tensors, Model building, training, and evaluation).
Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.
Sentiment analysis with RNNs, Transformers, and Huggingface Transformers (Deberta)
Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5...)
Machine translation with RNNs, attention, transformers, and Huggingface Transformers (T5)
Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)

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.

Enjoy!!!

Who this course is for:
Beginner Python Developers curious about Applying Deep Learning for Natural Language Processing in the domains of sentiment analysis and machine translation
Deep Learning for NLP Practitioners who want gain a mastery of how things work under the hood
NLP practitioners who want to learn how state of art sentiment analysis and machine translation models are built and trained using deep learning.
Anyone wanting to deploy ML Models
Learners who want a practical approach to Deep learning for Sentiment analysis and Machine Translation

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