Generative AI Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

This Generative AI training course, teaches practical applications for AI in the business environment. This course offers a combination of lectures and hands-on labs, providing participants with a solid understanding of AI concepts and the skills to design and implement AI solutions.

Throughout the course, you will learn about AI transformer-based architectures, the fundamentals of Python programming for AI deployments, and the deployment of open-source Transformer models. You will also explore the importance of hardware requirements in AI performance, comparing different GPU architectures and understanding how to match AI requirements with suitable hardware. The course delves into training techniques, including back propagation, gradient descent, and various AI tasks such as classification, regression, and clustering.

You will gain practical experience through hands-on exercises with open source LLM (Language Learning Model) frameworks, allowing you to work with fine-tuned models and run workloads on different models to understand their strengths and weaknesses. Additionally, the course covers the conversion of model formats and provides in-depth exploration of AI programming environments like PyTorch + transformers and transformers' low-level interactive inspection.

Towards the end of the course, you will delve into advanced topics such as context extension through fine-tuning and quantization for specific application target environments..

By attending Generative AI workshop, delegates will learn:

The Mechanics of Deep Learning
Transformer Architecture
Hardware Requirements
Using Llama to perform Natural Language Processing Tasks
Deploy a Natural Language Model
Model Fine Tuning

Basic Python skills.

The Generative AI class is ideal for:

Project Managers
Architects
CKA Developers
Data Acquisition Specialists

The Mechanics of Deep Learning

Choosing the latest AI models

What is current and what has already gone extinct?

Neural network architecture essentials

Tokenization
Embedding
Parameters: weights and bias
Nodes
Fully connected/Partially connected
Prompts and prompt engineering

The Transformer Model

Neural Network Architectures
Word embeddings

Importance in NLP
Word2Vec and GloVe embeddings
Contextualized embeddings (BERT, ELMo, etc.)

Self-attention mechanism and multi-head attention

Input Representation (Query, Key, and Value)
Computing Similarities
Attention Weights
Weighted Sum
Final Output

Positional encoding for attention
Transformer layers and stacking
Quantization
Effective LLM selection criteria and use-cases

Models: size, datasets, quantization, etc
Libraries
Frameworks

Hardware Requirements

GPUs role in AI performance (CPU vs GPU)
GPUs architecture history

Pascal, Turing, Ampere

Tensor core vs older GPU architectures
GPU vocabulary and the transformer

SM, tensors, tensor core, CUDA core, FP and INT cores, warp, threads

Current GPUs and cost vs value

Analysis of GPU specification sheets

GPU selection for models and workload
Quantizing for hardware performance and cost

Pre-trained LLM Model Essentials

Select and use pre-trained models to produce immediate results
Model ratings, metrics, leader boards
Model licensing and commercial use
Numbers every AI developer should know (cost, time, dataset size, etc.)
Synthetic data generation for model training.
Model training through feature extraction

Derive features from provided dataset
Use annotated data for fine-tuning training

Pre-trained LLM hands-on

Frameworks

llama.cpp, exllama, GPT4All

Evaluate multiple models, fine-tunings quantization

Falcon, Orca Mini, OpenLlama, Alpaca, MPT

Parameters
Fine tuned models
Prompts

Model expectations (instruct, chat, etc)
Extension improvement

Transformer Model training essentials

Overfitting
Regularization (Avoiding Overfitting)
Backpropagation
Gradient Descent
Embedding
Learning Rate
Perplexity
Batch Normalization
Warm-up and Learning Rate Decay
Loss Functions
Data Augmentation
Training Strategies
Evaluation Metrics

Conversion of Model Formats

PyTorch to ggml
JAX to ggml
F16 to 4bit quantization

Hands on with AI programming environments

PyTorch and transformers
Transformers low level interactive inspection

Model Fine Tuning

Perform fine-tuning in a hands-on environment
Demonstration project with good fine-tuning data to highlight frameworks

with llama.cpp
with PyTorch

Understanding fine tuning dataset formats
Data cleaning skills

Introduce Application Interfacing to Models

LangChain
Guidance for structured LLM output

Applications Augmentation with Langchain and Guidance

LangChain
Guidance for structured LLM output
Prompt engineering

Advanced Topics

Optimize your model through fine-tuning and quantization.
Realize quality and speed inference on specific workload
Context extension through fine tuning
LLM deployment

Use llama to perform Natural Language Processing tasks

Rewrite a classic poem of a well-known author in the tone of another well know author
Use Named Entity Recognition (NER) to identify Cajun food in recipes
Use domain specific models to improve project accuracy

Deploy a Natural Language Model Capstone

Build a full NLP project
Download, install and implement a trained NLP model
Deploy your NLP to perform the following tasks:

text classification
sentiment analysis
machine translation
chatbots
question answering

Encarta Labs Advantage

One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
All courses are delivered by Industry Veterans
Get jumpstarted from newbie to production ready in a matter of few days

Trained more than 50,000 Corporate executives across the Globe
All our trainings are conducted in workshop mode with more focus on hands-on sessions

Open Source Generative AI

COURSE AGENDA