Deep Learning- NLP for Sentiment analysis Translation 2024

Deep Learning: NLP for Sentiment analysis & Translation 2024, available at $64.99, has an average rating of 4.4, with 81 lectures, based on 22 reviews, and has 233 subscribers.

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

Enroll now: Deep Learning: NLP for Sentiment analysis & Translation 2024

Summary

Title: Deep Learning: NLP for Sentiment analysis & Translation 2024

Price: $64.99

Average Rating: 4.4

Number of Lectures: 81

Number of Published Lectures: 79

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 79

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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 Should Attend

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

Target Audiences

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

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!!!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Lecture 2: General intro

Lecture 3: Link to Code

Chapter 2: Tensors and variables

Lecture 1: Link to Dataset

Lecture 2: Basics

Lecture 3: Initialization and Casting

Lecture 4: Indexing

Lecture 5: Maths Operations

Lecture 6: Linear algebra operations

Lecture 7: Common methods

Lecture 8: Ragged tensors

Lecture 9: Sparse tensors

Lecture 10: String tensors

Lecture 11: Variables

Chapter 3: [PRE-REQUISCITE] Building neural networks with tensorflow

Lecture 1: Link to Code

Lecture 2: Task understanding

Lecture 3: Data preparation

Lecture 4: Linear regression model

Lecture 5: Error sanctioning

Lecture 6: Training and optimization

Lecture 7: Performance measurement

Lecture 8: Validation and testing

Lecture 9: Corrective measures

Lecture 10: TensorFlow Datasets

Chapter 4: Text Preprocessing for Sentiment Analysis

Lecture 1: Understanding Sentiment Analysis

Lecture 2: Text Standardization

Lecture 3: Tokenization

Lecture 4: One-hot encoding and Bag of Words

Lecture 5: Term frequency – Inverse Document frequency (TF-IDF)

Lecture 6: Embeddings

Chapter 5: Sentiment Analysis with Recurrent neural networks

Lecture 1: Link to Code

Lecture 2: How Recurrent neural networks work

Lecture 3: Data preparation

Lecture 4: Building and training RNNs

Lecture 5: Advanced RNNs (LSTM and GRU)

Lecture 6: 1D Convolutional Neural Network

Chapter 6: Sentiment Analysis with transfer learning

Lecture 1: Understanding Word2vec

Lecture 2: Integrating pretrained Word2vec embeddings

Lecture 3: Testing

Lecture 4: Visualizing embeddings

Chapter 7: Neural Machine Translation with Recurrent Neural Networks

Lecture 1: Link to Code

Lecture 2: Understanding Machine Translation

Lecture 3: Data Preparation

Lecture 4: Building, training and testing Model

Lecture 5: Understanding BLEU score

Lecture 6: Coding BLEU score from scratch

Chapter 8: Neural Machine Translation with Attention

Lecture 1: Link to Code

Lecture 2: Understanding Bahdanau Attention

Lecture 3: Building, training and testing Bahdanau Attention

Chapter 9: Neural Machine Translation with Transformers

Lecture 1: Link to Code

Lecture 2: Understanding Transformer Networks

Lecture 3: Building, training and testing Transformers

Lecture 4: Building Transformers with Custom Attention Layer

Lecture 5: Visualizing Attention scores

Chapter 10: Sentiment Analysis with Transformers

Lecture 1: Link to Code

Lecture 2: Sentiment analysis with Transformer encoder

Lecture 3: Sentiment analysis with LSH Attention

Chapter 11: Transfer Learning and Generalized Language Models

Lecture 1: Understanding Transfer Learning

Lecture 2: Ulmfit

Lecture 3: Gpt

Lecture 4: Bert

Lecture 5: Albert

Lecture 6: Gpt2

Lecture 7: Roberta

Lecture 8: T5

Chapter 12: Sentiment Analysis with Deberta in Huggingface transformers

Lecture 1: Link to Code

Lecture 2: Data Preparation

Lecture 3: Building,training and testing model

Chapter 13: Model Deployment

Lecture 1: Distillation

Lecture 2: Finetuning Distilled Model

Lecture 3: Converting TensorFlow Model to Onnx format

Lecture 4: Understanding of quantization

Lecture 5: Practical quantization of Onnx Model

Lecture 6: Undestanding APIs

Lecture 7: Building an NLP API with Fastapi

Lecture 8: Deploying API to the Cloud

Chapter 14: Neural Machine Translation with T5 in Huggingface transformers

Lecture 1: Link to Code

Lecture 2: Dataset Preparation

Lecture 3: Building,training and testing model

Instructors

Neuralearn Dot AI
Helping millions of learners, master Deep Learning.

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 3 votes

4 stars: 4 votes

5 stars: 13 votes

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