Large Language Models (LLM) & Transformers: Master Generative AI
Master the cutting-edge field of Large Language Models (LLM) and Transformer architectures with BinnBash Academy's comprehensive course. Learn attention mechanisms, pre-trained models (BERT, GPT), fine-tuning, prompt engineering, and LLM deployment. Build a powerful portfolio with intensive real-time live projects to become a cutting-edge LLM Engineer or AI Research Scientist!
Build Intelligent Language Systems!Who Should Enroll in this LLM & Transformers Course?
This course is ideal for individuals eager to dive deep into generative AI and advanced natural language processing:
- Aspiring LLM Engineers & AI Research Scientists specializing in Generative AI.
- Machine Learning Engineers looking to specialize in advanced NLP.
- Data Scientists seeking to implement and fine-tune large language models.
- Software Developers with Python experience interested in building AI-powered applications.
- Anyone with a strong mathematical and programming background eager to explore the frontier of AI.
LLM & Transformers Course Prerequisites
- Solid understanding of Python programming (intermediate to advanced).
- Foundational knowledge of Machine Learning and Deep Learning concepts.
- Basic understanding of neural networks (e.g., RNNs, CNNs).
- Familiarity with TensorFlow/Keras or PyTorch is highly recommended.
- A strong analytical and problem-solving mindset.
Key LLM & Transformers Tools & Concepts Covered
Hands-on mastery of leading LLM frameworks, advanced Transformer architectures, and deployment strategies for cutting-edge generative AI solutions.
LLM & Transformers: Comprehensive Syllabus & Intensive Real-Time Projects
Module 1: NLP Foundations & Sequence Models Revisited
- Brief Review: NLP Fundamentals, Text Preprocessing.
- Word Embeddings: Static vs. Contextual.
- Recurrent Neural Networks (RNNs), LSTMs, GRUs for sequential data.
- Encoder-Decoder Architectures (basic concepts).
- Challenges of traditional sequence models for long dependencies.
- Live Project: Implement and train a basic LSTM model for a sequence prediction task (e.g., next word prediction, simple text generation).
Tools & Concepts:
- Python, NLTK, SpaCy, TensorFlow/PyTorch, NumPy.
Expected Outcomes:
- Solidify NLP basics.
- Understand sequence model limitations.
- Prepare for Transformer concepts.
Module 2: The Transformer Architecture: Attention Is All You Need
- Introduction to the Transformer Model.
- Self-Attention Mechanism: Scaled Dot-Product Attention.
- Multi-Head Attention.
- Positional Encoding.
- Encoder and Decoder Stacks in detail.
- Feed-Forward Networks & Layer Normalization.
- Live Project: Implement a simplified Transformer encoder block from scratch (or using high-level framework components) and understand its internal workings.
Tools & Concepts:
- TensorFlow/PyTorch, mathematical foundations of attention.
Expected Outcomes:
- Deep understanding of Transformer components.
- Grasp the power of self-attention.
- Build foundational Transformer blocks.
Module 3: Pre-trained Transformers & Transfer Learning
- The Rise of Pre-trained Models: BERT, GPT, T5, RoBERTa, XLNet (architectural overview).
- Transfer Learning in NLP: Fine-tuning vs. Feature Extraction.
- Using the Hugging Face Transformers Library: Tokenizers, Models, Pipelines.
- Model Hub: Exploring available pre-trained models.
- Handling different NLP tasks with pre-trained models (classification, NER, Q&A).
- Live Project: Fine-tune a BERT-like model for a specific text classification task (e.g., spam detection, topic classification) on a custom dataset.
Tools & Concepts:
- Hugging Face Transformers, TensorFlow/PyTorch, various NLP datasets.
Expected Outcomes:
- Work with popular pre-trained models.
- Master fine-tuning techniques.
- Utilize the Hugging Face library effectively.
Module 4: Large Language Models (LLMs) & Generative AI
- What are LLMs? Scale, capabilities, and limitations.
- Generative Pre-trained Transformers (GPT-series concepts).
- Prompt Engineering: Crafting effective prompts for LLMs.
- Few-shot, One-shot, and Zero-shot Learning with LLMs.
- Techniques for LLM generation: Beam Search, Top-K, Nucleus Sampling.
- Introduction to Instruction Tuning & Reinforcement Learning from Human Feedback (RLHF) - concepts.
- Live Project: Experiment with a smaller open-source generative LLM (e.g., GPT-2, LLaMA-based) for text generation, summarization, and creative writing tasks using prompt engineering.
Tools & Concepts:
- Hugging Face Transformers (for generative models), prompt engineering techniques.
Expected Outcomes:
- Understand LLM principles and capabilities.
- Apply prompt engineering effectively.
- Generate diverse and coherent text.
Module 5: Advanced LLM Applications, Evaluation & Ethics
- LLMs for Code Generation & Understanding.
- LLMs for Multimodal Tasks (e.g., Image Captioning, Visual Question Answering - concepts).
- Retrieval-Augmented Generation (RAG) for factual accuracy and knowledge integration.
- Evaluating LLMs: Perplexity, BLEU, ROUGE, Human Evaluation.
- Ethical Considerations in LLMs: Bias, Hallucinations, Misinformation, Privacy.
- Live Project: Build a simple RAG system to answer questions based on a provided document corpus, demonstrating how to ground LLM responses in external knowledge.
Tools & Concepts:
- Hugging Face, vector databases (concepts), evaluation metrics.
Expected Outcomes:
- Apply LLMs to complex tasks.
- Evaluate LLM performance rigorously.
- Address ethical concerns in LLM development.
Module 6: LLM Deployment, MLOps & Intensive Capstone Projects
- Deployment Strategies for LLMs: API-based deployment (Flask/FastAPI), Model Serving (TensorFlow Serving/TorchServe, Triton Inference Server - concepts).
- Containerization with Docker for LLM applications.
- MLOps for LLMs: Versioning, Monitoring, CI/CD, A/B Testing.
- Cloud AI Services for LLM Deployment (AWS SageMaker, Google AI Platform, Azure ML - concepts).
- Cost Optimization for LLM Inference.
- Intensive Real-time Capstone Project: Develop and deploy an end-to-end LLM-powered application for a real client or a complex simulated problem. This could be a sophisticated chatbot, a content generation tool, a smart code assistant, or a complex Q&A system, integrating an LLM, building a user interface, and deploying it to a cloud environment.
- Building a Professional LLM & Transformers Portfolio: Showcasing deployed applications, prompt engineering techniques, and research contributions.
- Career Guidance: LLM Engineer, AI Research Scientist (Generative AI), Prompt Engineer, Applied AI Scientist, MLOps Engineer (LLM), Freelancing, Mock Interviews.
Tools & Concepts:
- Flask/FastAPI, Docker (concepts), Cloud platforms (concepts), MLOps tools.
- Intensive Live Project Work, Client Communication, Portfolio Building, Career Prep.
Expected Outcomes:
- Deploy LLMs into production.
- Understand MLOps principles for LLMs.
- Gain extensive practical experience with real-world LLM project lifecycle, leading to tangible, deployable Generative AI solutions.
- Prepare for a high-level LLM/Generative AI career.
This course provides hands-on, in-depth expertise to make you a proficient and job-ready LLM & Transformers professional, with a strong emphasis on advanced model building, real-time project implementation, and building a powerful, results-driven portfolio!
Large Language Models (LLM) & Transformers Professional Roles and Responsibilities in Real-Time Scenarios & Live Projects
Gain hands-on experience by working on live projects, understanding the real-time responsibilities of an LLM & Transformers professional in leading tech companies, AI research labs, and innovative startups. Our curriculum aligns with industry demands for cutting-edge Generative AI practitioners.
LLM Engineer
Designs, develops, and deploys large language models for various applications, as done at OpenAI.
AI Research Scientist (Generative AI)
Conducts research into new LLM architectures, training methodologies, and generative AI capabilities, similar to work at Google DeepMind.
Prompt Engineer
Specializes in crafting, optimizing, and managing prompts to elicit desired behaviors and outputs from LLMs, common at Anthropic.
NLP Engineer (LLM Focus)
Applies LLMs to solve complex natural language processing problems like advanced summarization, translation, and question answering, like at Hugging Face.
MLOps Engineer (LLM)
Focuses on streamlining the deployment, monitoring, and maintenance of large language models in production environments.
Applied AI Scientist (LLM)
Applies LLMs to develop innovative solutions for specific business domains and product features.
AI Solutions Architect (Generative AI)
Designs comprehensive system architectures that integrate LLMs and other generative AI components.
Conversational AI Developer
Builds advanced chatbots and virtual assistants powered by large language models.
Our Alumni Works Here!
Akash Verma
LLM Engineer
Priya Das
AI Research Scientist
Siddharth Jain
Prompt Engineer
Ananya Singh
NLP Engineer
Rohan Kumar
MLOps Engineer (LLM)
Kavya Reddy
Applied AI Scientist
Vijay Sharma
AI Solutions Architect
Meera Patel
Conversational AI Dev
Arjun Gupta
LLM Trainee
Divya Singh
Senior LLM Engineer
Akash Verma
LLM Engineer
Priya Das
AI Research Scientist
Siddharth Jain
Prompt Engineer
Ananya Singh
NLP Engineer
Rohan Kumar
MLOps Engineer (LLM)
Kavya Reddy
Applied AI Scientist
Vijay Sharma
AI Solutions Architect
Meera Patel
Conversational AI Dev
Arjun Gupta
LLM Trainee
Divya Singh
Senior LLM Engineer
What Our LLM & Transformers Students Say
"This LLM & Transformers course is a revelation! I now understand the core of generative AI and can build powerful language models."
"The deep dive into Transformer architecture and attention mechanisms was incredibly insightful. I feel confident tackling complex NLP problems."
"As an NLP enthusiast, this course was exactly what I needed to master LLMs. Prompt engineering and fine-tuning techniques were invaluable."
"BinnBash Academy's focus on LLM deployment and MLOps truly sets it apart. I gained practical experience essential for production-ready generative AI."
"The instructors are highly knowledgeable and provide cutting-edge insights into ethical AI and advanced LLM applications like RAG."
"I highly recommend this course for anyone looking to build a career in generative AI. It's comprehensive, challenging, and prepares you for the future of AI."
"From mastering Hugging Face to understanding different LLM generation strategies, every aspect was covered in detail. I feel fully prepared for a top-tier AI role."
"The emphasis on building a professional portfolio with deployed LLM applications and career guidance was extremely helpful. BinnBash truly supports your job search."
"Learning about LLMs for code generation and multimodal tasks opened up a new world of possibilities for me."
"The practical approach to learning, combined with advanced theory and intensive real-time projects, made this course stand out from others."
LLM & Transformers Job Roles After This Course
LLM Engineer
AI Research Scientist (Generative AI)
Prompt Engineer
NLP Engineer (LLM Focus)
MLOps Engineer (LLM)
Applied AI Scientist (LLM)
AI Solutions Architect (Generative AI)
Conversational AI Developer