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