Library
Cuantum trackFull access

NLP with Transformers: Advanced Techniques and Multimodal Applications

15 chapters and 93 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
15
Reading time
~ 14h
Level
Professional
Language
English
Edition
2025
Your progress0%
Start reading

Chapters & sections

15 chapters - 93 sections

Chapter 1: Advanced NLP Applications

0/5
1.1 Machine Translation56m1.2 Text Summarization (Extractive and Abstractive)63m1.3 Text Generation with GPT Models42m1.4 Practical Exercises4mChapter 1 Summary3m

Project 1: Machine Translation with MarianMT

0/8
Base Project Implementation6mStep 1: Setting Up the Environment1mStep 2: Loading the MarianMT Model2mStep 3: Translating Text6mStep 4: Exploring Additional Language Pairs2mAdvanced Customizations4mDeploying a Translation API4mConclusion2m

Project 2: Text Summarization with T5

0/8
Base Project Implementation7mStep 1: Setting Up the Environment1mStep 2: Loading the T5 Model2mStep 3: Summarizing Text9mStep 4: Adjusting Hyperparameters3mStep 5: Summarizing Longer Texts9mIndustry Applications1mConclusion2m

Quiz Part I

0/4
Multiple Choice Questions2mTrue or False1mShort Answer Questions1mAnswer Key2m

Chapter 2: Hugging Face and Other NLP Libraries

0/5
2.1 Overview of the Hugging Face Ecosystem30m2.2 Hugging Face Transformers and Datasets Libraries37m2.3 TensorFlow and PyTorch for NLP16m2.4 Practical Exercises6mChapter Summary3m

Chapter 3: Training and Fine-Tuning Transformers

0/5
3.1 Data Preprocessing for Transformer Models22m3.2 Fine-Tuning Techniques: LoRA and Prefix Tuning25m3.3 Evaluation Metrics: BLEU, ROUGE, BERTScore35m3.4 Practical Exercises5mChapter Summary3m

Chapter 4: Deploying and Scaling Transformer Models

0/5
4.1 Real-Time Inferencing with ONNX and TensorFlow Lite18m4.2 Deploying Models on Cloud Platforms19m4.3 Scalable APIs with FastAPI and Hugging Face Spaces15m4.4 Practical Exercises6mChapter Summary3m

Project 3: Sentiment Analysis API with Fine-Tuned Transformer

0/9
Steps to Build the Sentiment Analysis API3mStep 1: Install Required Libraries1mStep 2: Load and Preprocess the Dataset3mStep 3: Fine-Tune the Model3mStep 4: Build the FastAPI Application3mStep 5: Run the API2mStep 6: Deploy the API (Optional)3mApplications of This Project3mConclusion2m

Project 4: Named Entity Recognition (NER) Pipeline with Custom Fine-Tuning

0/9
Steps to Build the NER Pipeline4mStep 1: Install Required Libraries1mStep 2: Load and Preprocess the Dataset2mStep 3: Tokenize the Dataset3mStep 4: Fine-Tune the Model3mStep 5: Build the NER Pipeline2mStep 6: Deploy the NER Pipeline as an API4mApplications of This Project2mConclusion2m

Quiz Part II

0/4
Multiple-Choice Questions2mTrue or False1mShort-Answer Questions1mAnswer Key2m

Chapter 5: Innovations and Challenges in Transformers

0/5
5.1 Large Language Models: GPT-4, Claude, LLaMA29m5.2 Efficient Transformers: Reformer, BigBird, LongFormers34m5.3 Ethical AI: Bias and Fairness in Language Models43m5.4 Practical Exercises5mChapter Summary3m

Chapter 6: Multimodal Applications of Transformers

0/5
6.1 Vision-Language Models (CLIP, Flamingo)64m6.2 Speech Recognition with Whisper36m6.3 Multimodal AI: Integration of Text, Image, and Video40m6.4 Practical Exercises5mChapter Summary3m

Project 5: Multimodal Medical Image and Report Analysis with Vision-Language Models

0/8
Steps to Build the System4mStep 1: Install Required Libraries1mStep 2: Load and Preprocess the Data3mStep 3: Use CLIP for Image-Text Matching3mStep 4: Generate Captions for Medical Images2mApplications of This System5mChallenges and Considerations5mConclusion2m

Project 6: Multimodal Video Analysis and Summarization

0/9
Steps to Build the System3mStep 1: Install Required Libraries1mStep 2: Extract Video Frames3mStep 3: Transcribe Audio from Video3mStep 4: Perform Video Frame Analysis2mStep 5: Generate Video Summaries3mApplications of This System3mChallenges and Considerations2mConclusion2m

Quiz Part III

0/4
Multiple-Choice Questions2mTrue or False1mShort-Answer Questions1mAnswer Key2m