Generative AI Training in Nepal
Become an Expert in Generative AI – Build AI Models and Create New Possibilities
Generative AI Training in Nepal teaches you how to use AI. You’ll learn to create realistic images, videos, text, and music. This course is for everyone, from beginners to those with some AI experience. It will give you the skills to create advanced generative models. You’ll learn how to transform industries using automation and creativity.
Why Choose Generative AI Training?
Generative AI is an exciting and fast-growing area in artificial intelligence. It lets machines create new data. This mimics human creativity in art, design, and content generation. Generative AI is changing many industries, like entertainment, healthcare, finance, and marketing. It uses tools such as GANs (Generative Adversarial Networks) to create lifelike images. It also develops text-based apps with advanced models like GPT.
Join our Generative AI Training in Nepal. You'll learn to use AI to create realistic outputs. This will help you build your own AI-driven creative projects. Skilled workers in generative AI are in high demand worldwide, even in Nepal.
What You Will Learn
In this training, you’ll learn the basics of Generative AI. You’ll see how these models are made and used. You'll use tools and techniques from industry experts. You'll learn through hands-on projects and guidance.
First, you will learn the basics of GANs and VAEs. Then, you'll discover how to train these models well. The course will focus on practical uses such as generating images, text, and audio.
Why Choose Our Generative AI Training?
- Innovative Training: We give you the newest tools and methods for generative AI.
- Expert Instructors: Learn from industry professionals who bring real-world experience to the classroom.
- Hands-On Learning: Work on real-world projects and develop your own generative AI applications.
- Career Advancement: Skills from this course will help you get jobs in AI, data science, and creative fields.
Join our Generative AI Training in Nepal. This is your first step to becoming an expert in a thrilling and rewarding tech field. Unlock your potential and start creating the future of AI-driven content!
Materials included
Requirements
Course Syllabus
Introduction to Generative AI
- Overview of AI: What is AI?
- What is Generative AI?
- -- Text generation
- --Image synthesis
- --Music creation
- Applications and Impact of Generative AI
- -- Examples in various industries (art, healthcare, etc.)
Basics of Machine Learning and Data Preparation
- Machine Learning Overview
- --Supervised vs Unsupervised Learning
- -- Neural networks
- Data Preparation and Preprocessing
- -- Cleaning and preprocessing data
- -- Splitting datasets (train, validation, test)
- -- Normalization and standardization
- Hands-on: Preprocess the MNIST dataset and train a simple feedforward network using TensorFlow/PyTorch.
Gradient Descent and Backpropagation
- How neural networks learn
- -- Cost functions, gradients, and optimization
- -- Gradient Descent
- -- Backpropagation
- Hands-on: Implement gradient descent for a simple network
Deep Learning Frameworks
- Overview of TensorFlow and PyTorch
- Hands-on: Set up TensorFlow or PyTorch
- -- Create a basic neural network.
Training and Validation
- Overfitting, underfitting, and regularization techniques
- Hyperparameter tuning
- Hands-on: Train a model on the MNIST dataset.
Introduction to Generative Models
- What are generative models?
- -- Autoencoders, Variational Autoencoders (VAEs), GANs, Transformers
- -- Discriminative vs Generative models
- Hands-on: Build a simple autoencoder.
Hands-on: Build a simple autoencoder.
- How GANs work: Generator and discriminator interplay
- Hands-on: Generate simple images using a GAN (MNIST dataset).
- Recap with a quiz to reinforce concepts.
NLP Basics and Language Models
- Tokenization, embeddings, and sequence-to-sequence models
- Introduction to LSTM and RNNs (limitations)
- Pre-trained embeddings (e.g., Word2Vec, GloVe)
- Hands-on: Generate text using an LSTM model and integrate pre-trained embeddings.
Transformer Models
- Key concepts: Self-attention, encoder-decoder architecture
- Hands-on: Build a mini-transformer for text generation.
GPT Models and Text Generation
- Introduction to GPT-2 and GPT-3
- Hands-on: Use Hugging Face to generate coherent text with GPT-2
- Optional: Fine-tune GPT-2 on a small custom dataset.
Ethical Considerations in Text Generation
- Biases in generative models
- Case studies on ethical issues and mitigation strategies.
Image Synthesis Basics
- Introduction to Convolutional Neural Networks (CNNs)
- Overview of GAN-based image generation
- Hands-on: Generate digit images using DCGAN.
- Basics of Image Augmentation (flipping, cropping, rotation).
Advanced GANs
- Conditional GANs (cGANs) and StyleGANs
- Hands-on: Modify image styles using StyleGAN.
Diffusion Models and DALL-E
- Basics of diffusion models
- Role in image generation
- Introduction to DALL-E and text-to-image models
- Hands-on: Generate images with DALL-E or an open-source equivalent.
Discussion and Review
- Compare GANs, Diffusion models, and Transformers
- Real-world applications for each.
introduction to LangChain and Document Handling
- What is LangChain?
- --Benefits for generative AI applications
- Basics of vector embeddings
- -- What are vector embeddings?
- -- How they are generated and stored in vector databases
- Hands-on
- -- Generate embeddings using a pre-trained model and visualize them
- -- Build a pipeline to load and preprocess documents using LangChain.
Retrieval-Augmented Generation (RAG)
- Understanding RAG: Combining retrieval and generation for improved accuracy
- Hands-on
- -- Set up a vector database (e.g., FAISS, Pinecone)
- --Build a chatbot that retrieves relevant info from documents
Capstone Project with LangChain and RAG
- Students integrate LangChain and RAG into their capstone projects
- -- Example: Build a domain-specific chatbot or knowledge assistant.
Deployment of LangChain Applications
- Deploy RAG-powered applications using Streamlit or Flask
- Hands-on: Create a simple web app for the chatbot or document assistant.
Feedback, Iteration, and Final Presentations
- Gathering and incorporating feedback into projects
- Iterative improvement of applications
- Students showcase their LangChain and RAG projects
Wrap-Up and Future Directions
- Discuss the future of LangChain and RAG in AI applications
- Share resources for further learning and research in the field.
