Deep Learning: Natural Language Processing with Transformers
Deep Learning: Natural Language Processing with Transformers, available at $69.99, has an average rating of 4.41, with 205 lectures, based on 210 reviews, and has 2066 subscribers.
You will learn about The Basics of Tensors and Variables with Tensorflow Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle. Basics of Tensorflow and training neural networks with TensorFlow 2. 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 Recurrent Neural Networks, Modern RNNs, training sentiment analysis models with TensorFlow 2. Intent Classification with Deberta in Huggingface transformers Conversion from tensorflow to Onnx Model Building API with Fastapi Deploying API to the Cloud Neural Machine Translation with T5 in Huggingface transformers Extractive Question Answering with Longformer in Huggingface transformers E-commerce search engine with Sentence transformers Lyrics Generator with GPT2 in Huggingface transformers Grammatical Error Correction with T5 in Huggingface transformers Elon Musk Bot with BlenderBot in Huggingface transformers This course is ideal for individuals who are Python Developers curious about Deep Learning for NLP or Deep Learning Practitioners who want gain a mastery of how things work under the hoods or Anyone who wants to master deep learning fundamentals and also practice deep learning for NLP using best practices in TensorFlow. or Natural Language Processing practitioners who want to learn how state of art NLP 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 Natural Language Processing It is particularly useful for Python Developers curious about Deep Learning for NLP or Deep Learning Practitioners who want gain a mastery of how things work under the hoods or Anyone who wants to master deep learning fundamentals and also practice deep learning for NLP using best practices in TensorFlow. or Natural Language Processing practitioners who want to learn how state of art NLP 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 Natural Language Processing.
Enroll now: Deep Learning: Natural Language Processing with Transformers
Summary
Title: Deep Learning: Natural Language Processing with Transformers
Price: $69.99
Average Rating: 4.41
Number of Lectures: 205
Number of Published Lectures: 121
Number of Curriculum Items: 205
Number of Published Curriculum Objects: 121
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- The Basics of Tensors and Variables with Tensorflow
- Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle.
- Basics of Tensorflow and training neural networks with TensorFlow 2.
- 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
- Recurrent Neural Networks, Modern RNNs, training sentiment analysis models with TensorFlow 2.
- Intent Classification with Deberta in Huggingface transformers
- Conversion from tensorflow to Onnx Model
- Building API with Fastapi
- Deploying API to the Cloud
- Neural Machine Translation with T5 in Huggingface transformers
- Extractive Question Answering with Longformer in Huggingface transformers
- E-commerce search engine with Sentence transformers
- Lyrics Generator with GPT2 in Huggingface transformers
- Grammatical Error Correction with T5 in Huggingface transformers
- Elon Musk Bot with BlenderBot in Huggingface transformers
Who Should Attend
- Python Developers curious about Deep Learning for NLP
- Deep Learning Practitioners who want gain a mastery of how things work under the hoods
- Anyone who wants to master deep learning fundamentals and also practice deep learning for NLP using best practices in TensorFlow.
- Natural Language Processing practitioners who want to learn how state of art NLP models are built and trained using deep learning.
- Anyone wanting to deploy ML Models
- Learners who want a practical approach to Deep learning for Natural Language Processing
Target Audiences
- Python Developers curious about Deep Learning for NLP
- Deep Learning Practitioners who want gain a mastery of how things work under the hoods
- Anyone who wants to master deep learning fundamentals and also practice deep learning for NLP using best practices in TensorFlow.
- Natural Language Processing practitioners who want to learn how state of art NLP models are built and trained using deep learning.
- Anyone wanting to deploy ML Models
- Learners who want a practical approach to Deep learning for Natural Language Processing
Deep Learning is a hot topic today! This is because of the impact it’s having in several industries. One of the fields in which deep learning has the most influence today is Natural Language Processing.
To understand why Deep Learning based Natural Language Processingis so popular; it suffices to take a look at the different domains where giving a computer the power to understand and make sense out of text and generate text has changed our lives.
Some applications of Natural Language Processing are in:
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Helping people around the world learn about any topic ChatGPT
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Helping developers code more efficiently with Github Copilot.
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Automatic topic recommendation in our Twitter feeds
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Automatic Neural Machine Translation with Google Translate
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E-commerce search engines like those of Amazon
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Correction of Grammar with Grammarly
The demand for Natural Language Processing engineers is skyrocketing and experts in this field are highly paid, because of their value.However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don’t take the beginners into consideration 🙁
In this course, we shall take you on an amazing journey in which you’ll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world’s most popular library for deep learning, built by Google) and Huggingface transformers(most popular NLP focused library ).We shall start by understanding how to build very simple models (like Linear regression model for car price prediction and RNN text classifiers for movie review analysis) using Tensorflow to much more advanced transformer models (like Bert, GPT, BlenderBot, T5, Sentence Transformers and Deberta).
After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep learning for NLP solutions that big tech companies encounter.
You will learn:
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The Basics of Tensorflow (Tensors, Model building, training, and evaluation)
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Text Preprocessing for Natural Language Processing.
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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 Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)
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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)
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Intent Classification with Debertain Huggingface transformers
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Named Entity Relation with Roberta in Huggingface transformers
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Neural Machine Translation with T5 in Huggingface transformers
-
Extractive Question Answering with Longformer in Huggingface transformers
-
E-commerce search engine with Sentence transformers
-
Lyrics Generator with GPT2 in Huggingface transformers
-
Grammatical Error Correction with T5 in Huggingface transformers
-
Elon Musk Bot with BlenderBot in Huggingface transformers
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Speech recognition with RNNs
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: intro
Lecture 1: Welcome
Lecture 2: General Introduction
Lecture 3: About this Course
Lecture 4: Link to Code
Chapter 2: [PRE-REQUISITE] Tensors and Variables
Lecture 1: Link to Code
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-REQUISITE] Building Neural Networks with TensorFlow
Lecture 1: Link to Code
Lecture 2: Link to Dataset
Lecture 3: Task Understanding
Lecture 4: Data Preparation
Lecture 5: Linear Regression Model
Lecture 6: Error Sanctioning
Lecture 7: Training and Optimization
Lecture 8: Performance Measurement
Lecture 9: Validation and Testing
Lecture 10: Corrective Measures
Lecture 11: 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: Understanding Distillation
Lecture 2: Finetuning Distilled Model
Lecture 3: Converting Tensorflow model to Onnx format
Lecture 4: Understanding quantization
Lecture 5: Practical quantization of Onnx model
Lecture 6: What is an API
Lecture 7: Building an API with FastAPI
Lecture 8: Deploying API to cloud
Chapter 14: Intent Classification with Deberta in Huggingface transformers
Lecture 1: Link to Code
Lecture 2: Problem Understanding and Data Preparation
Lecture 3: Building,training and testing model
Chapter 15: Named Entity Relation with Roberta in Huggingface transformers
Lecture 1: Link to Code
Lecture 2: Problem Understanding and Data Preparation
Lecture 3: Building,training and testing model
Chapter 16: Neural Machine Translation with T5 in Huggingface transformers
Instructors
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Neuralearn Dot AI
Helping millions of learners, master Deep Learning.
Rating Distribution
- 1 stars: 8 votes
- 2 stars: 14 votes
- 3 stars: 19 votes
- 4 stars: 49 votes
- 5 stars: 120 votes
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