Crack any interview with all prepration

Campus for good Pre-training

Tokenization

Scaling

Conditioning

How to try out these models

What are text-to-image models?

How can we mitigate LLM limitations?

What is an LLM app?

What is LangChain?

Exploring key components of LangChain

What are chains?  What are agents?

What is memory?

What are tools?

How does LangChain work?

Comparing LangChain with other frameworks

Getting Started with LangChain

pip

Poetry

Conda

Docker

Exploring API model integrations

OpenAI

Hugging Face

Google Cloud Platform

Jina AI

Replicate  Others

Azure

Anthropic

Exploring local models

Hugging Face Transformers

llama.cpp

GPT4All

Building an application for customer service

Hallucinations

Prompt templates

Chain of density

Map-Reduce pipelines

Monitoring token usage

Extracting information from documents

Information retrieval with tools

Building a visual interface

Exploring reasoning strategies

Understanding retrieval and vectors

Embeddings

Vector storage

Vector indexing

Vector libraries

Vector databases

Loading and retrieving in LangChain

Document loaders

Retrievers in LangChain

kNN retriever

PubMed retriever

Custom retrievers

Implementing a chatbot

Document loader

Vector storage

Memory

Conversation buffers

Remembering conversation summaries

Storing knowledge graphs

Combining several memory mechanisms

Long-term persistence

Moderating responses

Developing Software with Generative AI

Software development and AI

Code LLMs

Writing code with LLMs

StarCoder

StarChat

Llama

Small local model

Automating software development

LLMs for Data Science

The impact of generative models on data science

Automated data science

Data collection

Visualization and EDA

Preprocessing and feature extraction

AutoML

Using agents to answer data science questions

Data exploration with LLMs

Customizing LLMs and Their Output

Conditioning LLMs

Methods for conditioning

Reinforcement learning with human feedback

Low-rank adaptation

Inference-time conditioning

Fine-tuning

Setup for fine-tuning

Open-source models

Commercial models

Prompt engineering

Prompt techniques

Zero-shot prompting

Few-shot learning

Chain-of-thought prompting

Self-consistency

Tree-of-thought

Generative AI in Production

How to get LLM apps ready for production

Terminology

How to evaluate LLM apps

Comparing two outputs

Comparing against criteria

String and semantic comparisons

Running evaluations against datasets

How to deploy LLM apps

FastAPI web server

Ray

How to observe LLM apps

Tracking responses

Observability tools

LangSmith

PromptWatch