Roadmap To Learn Generative AI

Becoming a generative AI engineer involves mastering several key areas of artificial intelligence and machine learning. 

Here’s a detailed roadmap to guide you through the process:

Step 1: Mathematics Essentials for Machine Learning

Before mastering machine learning, it is important to understand the fundamental mathematical concepts that power these algorithms.

Linear Algebra: Crucial for algorithms like deep learning. Includes vectors, matrices, determinants, eigenvalues, eigenvectors, vector spaces, and transformations.

Calculus: Used to optimize functions in machine learning. Involves derivatives, integrals, limits, series, multivariable calculus, and gradients.

Probability and Statistics: Key for learning from data and predictions. Covers probability theory, random variables, distributions, expectations, variance, covariance, correlation, hypothesis testing, confidence intervals, maximum likelihood estimation, and Bayesian inference.

Below are some of the best resources on the internet to learn Mathematics

• 3Blue1Brown — The Essence of Linear Algebra: Series of videos that give a geometric intuition to these concepts.
• StatQuest with Josh Starmer — Statistics Fundamentals: Offers simple and clear explanations for many statistical concepts.

Step 2: Python for Machine Learning

Python is a powerful programming language widely used in machine learning due to its clarity, consistency, and rich set of data science tools.

• Python Basics: It’s important to grasp Python’s fundamental syntax, data types, handling errors, and OOPs concepts.
• Data Science Libraries: Knowing NumPy for numerical operations, Pandas for organizing and analyzing data, and tools like Matplotlib and Seaborn for visualizing data is essential.
• Data Pre-processing: Before using data for learning, it’s crucial to scale features, handle missing data, spot outliers, convert categorical data, and split data into training, validation, and test groups.
• Machine Learning Libraries: Mastery of Scikit-learn is key—it offers various algorithms for supervised and unsupervised learning. You’ll need to implement models like linear regression, logistic regression, decision trees, random forests, K-nearest neighbors (K-NN), and K-means clustering. Techniques like PCA and t-SNE help reduce data dimensions for better understanding.

Below are some of the best resources on the internet to learn Python:

• freeCodeCamp — Learn Python: Long video that provides a full introduction into all of the core concepts in Python.
• Python Data Science Handbook: Free digital book that is a great resource for learning pandas, NumPy, matplotlib, and Seaborn.

Step 3: Learn Deep Learning

Neural networks are key in many machine learning models, especially in deep learning. Understanding how they work is crucial for using them effectively.

• Fundamentals: Know the basics of neural network structure—layers, weights, biases, and activation functions like sigmoid and ReLU.
• Training and Optimization: Learn about backpropagation to adjust network weights, and different loss functions like Mean Squared Error (MSE) and Cross-Entropy. Understand optimization methods such as Gradient Descent, Stochastic Gradient Descent, RMSprop, and Adam.
• Overfitting: Understand overfitting—when a model fits training data too closely but performs poorly on new data. Techniques to prevent this include dropout, L1/L2 regularization, early stopping, and data augmentation.
• Implementing an MLP: Create a Multilayer Perceptron (MLP), a type of neural network, using PyTorch to deepen your understanding.

Resources to Learn Deep learning

• 3Blue1Brown — But what is a Neural Network?: This video gives an intuitive explanation of neural networks and their inner workings.
• Patrick Loeber — PyTorch Tutorials: Series of videos for complete beginners to learn about PyTorch.

Step 4: Natural Language Processing (NLP)

NLP is a fascinating part of AI that connects human language with computers. It’s used in many applications like translation, chatbots, and sentiment analysis.

• Text Preprocessing: Learn steps like splitting text into words (tokenization), reducing words to their roots (stemming), and considering context (lemmatization). Remove common words (stop words) too.
• Feature Extraction: Convert text for machine learning. Methods include Bag-of-words (BoW), TF-IDF (importance of words), and n-grams (word sequences).
• Word Embeddings: Represent words to show similar meanings. Techniques like Word2Vec, GloVe, and FastText are used.
• Recurrent Neural Networks (RNNs): Understand RNNs, which handle sequences like sentences. Learn about LSTMs and GRUs, which can learn relationships between words over long texts.

Resources to learn Learn NLP

• Kaggle — NLP Guide: Notebooks and resources with explanations of NLP.
• Jay Alammar Blog: A good reference to understand the famous  research paper.
• colah’s blog — Understanding LSTM Networks: A more theoretical article about the RNN & LSTM network.
• Gokul raja github : NLP course with a roadmap and notebooks to get into Large Language Models (LLMs).

Step 5: The Transformer Architecture

The Transformer model, from the “Attention is All You Need” paper, is a key part of large language models. It’s essential for understanding how machines process language.

• Attention Mechanisms: Learn about attention, which lets the model focus on parts of input. Includes self-attention and scaled dot-product attention.
• Tokenization: Convert text into machine-readable format by splitting into tokens (words or parts of words).
• Transformer Architecture: Explore its parts like positional encoding (showing word positions), multi-head attention (multiple ways to focus), feed-forward networks (processing info), and normalization (standardizing data).
• Decoding Strategies: Understand how the model creates output. Includes greedy decoding (simple), beam search (choosing best options), and top-k sampling (picking from top choices).

Resources to learn Transformer Architecture

• The Illustrated Transformer by Jay Alammar: This is a visual and intuitive explanation of the Transformer model.
• Hugging Face — NLP Course: An excellent mini-course that goes beyond the Transformer architecture.
• Harvard — The Annotated Transformer: An excellent in-depth article about the original Transformer paper.
• Introduction to the Transformer by Rachel Thomas: Provides a good intuition behind the main ideas of the Transformer architecture.
• Stanford CS224N — Transformers: A more academic presentation of this architecture.
• Read Research Paper as well ( If you want to understand core mathematics )

Step 6: Pre-trained Language Models

Models like BERT, GPT-2, and T5 are powerful tools for tasks such as text classification, generation, summarization, and question answering.

• BERT: Learn its bidirectional training, which helps understand context. Fine-tune BERT for tasks like classifying sequences or answering questions.
• GPT-2: It uses a decoder-only design for generating text. Understand its pre-training and use it for text creation.
• T5: A versatile model for tasks like classification, translation, and summarization. Learn about its “prefix-tuning” method and training for different tasks.
• Model Evaluation: Assess performance using metrics like accuracy, F1 score (for classification), BLEU score (for translation), or perplexity (for language generation).

Resources to learn Pre-trained Language Models

• The Illustrated BERT by Jay Alammar: Another excellent visual guide to the BERT architecture.
• The Illustrated GPT-2 by Jay Alammar: In-depth illustrated guide to the GPT-2 architecture.
• T5 paper: The original paper that introduced the T5 model and many essential concepts for modern NLP.

Step 7: Practical Applications and Tools

• OpenAI API usage
• Gemini API usage
• Hugging Face Transformers library
• LangChain & LlamaIndex for building AI applications
• Hands-on project: Create a simple chatbot or text summarization tool

Best Resources ( Learn from Official Documentation )

Step 8: Advanced Language Modeling

To enhance your skills, focus on creating embeddings with Sentence Transformers, storing them in vector databases, and fine-tuning Language Models (LLMs) using efficient learning techniques.

• Sentence Transformers: Models that create meaningful representations of sentences or texts. Learn to store these embeddings in a vector database for quick similarity searches.
• Fine-Tuning LLMs: Customize pre-trained models for specific tasks or domains like medical text analysis or sentiment analysis on movie reviews by training them on relevant datasets.
• Parameter-Efficient Learning: Discover techniques like LoRA for training models effectively with less data and computational power, making learning more efficient and accessible.

Resources to learn Advanced Language Modeling

• Pinecone — Sentence Transformers: Mini-book about NLP for semantic search in general.
• Hugging Face — PEFT: Another library from Hugging Face implementing different techniques, such as LoRA.

Step 9: LLMOps: Large Language Model Operations

Delve into Large Language Model Operations (LLMOps), focusing on prompt engineering, framework building with LangChain and Llamaindex, and optimizing inference using techniques like weight quantization, pruning, and distillation.

• Fine-tuning LLaMA: Popular for customizing models like LLaMA for specific tasks, requiring efficient learning methods such as QLoRA due to their size and training complexities.
• Build LLM Frameworks: Utilize frameworks like LangChain and LlamaIndex to integrate large language models into system design, enabling vector database queries and enhancing model functionality.
• Optimization Techniques for Inference: Ensure efficient model use with techniques like weight quantization (e.g., 4-bit, 3-bit), pruning, and knowledge distillation as LLMs grow in size.
• LLM Deployment: Deploy models locally (e.g., llama.cpp) or in the cloud (e.g., Hugging Face’s text generation) for various applications, enhancing accessibility and performance.

Resources to learn LLMOps

• Beginner level Generative AI course : In this course they’ll cover LLM, Generative AI, Fine tuning, RLHF
• Advanced Gen AI short course :In this courses by Deep learning AI(langchain, prompt engineering, RAG, Evaluation and monitoring Gen AI)
• Full stack LLMOPS course : this course cover end to end llmops
• Cohere LLM course : This blog based LLM course covers almost the basics of LLM.
• MLExpert — Fine-tuning Alpaca: Guide to fine-tune LLaMA on a custom dataset.

Summary

This Generative AI roadmap includes various topics starting from neural networks to LLM model deployment. This guide aims to help you navigate the steps to learn generative AI effectively by mastering essential skills.

I hope you enjoyed this article on the Generative AI Roadmap with learning resources. Feel free to ask any questions in the comments below!