Generative AI Learning Roadmap for 2024

Generative AI (GenAI) is the buzzword reshaping the landscape of innovation today. Imagine a world where computers can paint masterpieces, compose symphonies, and craft engaging stories—all from scratch.

From building lifelike images that blur the lines between reality and fantasy to writing text that feels as though it was penned by a human hand, GenAI is at the forefront of these groundbreaking advancements.

This transformative technology is not just a fleeting trend; it is redefining the boundaries of creativity and automation, making it one of the most exciting developments in the tech world today.

Let's understand what skills are needed to learn Generative AI.

1. Basic Mathematics and statistics
2. Python Programming language and its libraries
3. Machine Learning models
4. Deep Learning - Neural Network models
5. Natural Language Processing
6. Transformers models and LLMs
7. Pre-Trained Generative AI models
8. Advanced LLMs - RAG and Fine-Tuning

1. Basic Mathematics and Statistics

Before learning machine learning models, it is important to understand the fundamental concepts of mathematics and statistics.

• Linear Algebra: Understanding vectors, matrices, and transformations is crucial for neural network operations.
• Calculus: Concepts like derivatives, limits and integrals are used to optimize algorithms of continuous functions.
• Probability and Statistics: Essential for understanding data distributions, model evaluations, and probabilistic models.

2. Python Programming and its Libraries

The most programming language used in AI and Machine Learning is Python, due its flexibility, easy-to-code and vast number of libraries available.

• Python Basics: Data types, array manipulation, string manipulation, dictionaries, list, sets and object oriented programming.
• Data Science Libraries: NumPy used for numerical operations and Pandas used for data manipulation and analysis. Matplotlib and Seaborn is a must for data visualization, which helps to figure out the outliers and missing data in datasets.
• Data Preprocessing: Having knowledge in data cleaning and data normalization is a must

3. Machine Learning models

Machine Learning is process of training machine to learn from the dataset. Having basic knowledge in machine learning helps to understand more complex models.

• Supervised Learning:
  • Training our machine with labelled datasets.
  • Algorithms such as - classification and regression algorithms used in this type of machine learning.
  • Commonly used algorithms - Linear regression, Logistic regression, SVM, K-Nearest Neighbour, Naive Bayes, random Forest and Decision Trees.
• Unsupervised Learning:
  • Training our machine with unlabelled datasets and predicts output based on pattern.
  • Algorithms such as - clustering, association rule and dimensionality reduction used in this type of machine learning.
  • Commonly used algorithms - K-Means clustering, Hierarchical clustering, Aprori, FP-Growth and Principal Component Analysis (PCA).

4. Neural Networks - Deep Learning

Neural networks mimics human brain to understand and predicts images, text and processing a language.

• Important libraries: Some of the important libraries for implementing neural networks - PyTorch and TensorFlow libraries.
• Basic neural networks: Understanding layers, weights and activation functions. Algorithms such as - forward and backward propagation, single and multi layer perceptron and feed-forward layer.
• Artificial neural network: Gradient descent, Recurrent neural network and convolutional neural networks.

5. Natural Language Processing - NLP

Most excited field of artificial intelligence is natural language processing. From simple text processing to understanding linguistic texts, natural language processing plays a vital role in processing, extracting and understanding human language.

• Libraries: Many libraries are available to learn NLP such as - Natural Language processing Toolkit (NLTK), SpaCy, Gensim and many more.
• Text Preprocessing: Using the above libraries we can preprocess the text like tokenization (splitting text/sentences to words), stemming (find 'root' text), lemmatization (find 'root' text), parts-of-speech tags and named entity recognition.
• Feature Extraction: Techniques such as - One-Hot Encoding, Bag Of Words (BOW) and TF-IDF helps to identify similar texts and syntactic meaning of text for feature extraction.
• Word Embeddings: Word embeddings means numerical representation of words to real-valued vectors. Techniques such as - Word2Vec, GloVe and FastText. Word2Vec is the most popular word embedding which uses continuous bag-of-words (CBOW) and some deep learning models like RNN.

6. Transformer Models

The seminal 2017 paper "Attention Is All You Need" by Google introduced the transformer model. Transformer models leverage the self-attention mechanism to process text, images, and speech in real-time, without relying on traditional recurrent or convolutional neural networks. This innovation has revolutionized the field of deep learning and has become a cornerstone for numerous applications in natural language processing, computer vision, and speech recognition.

7. Pre-trained Generative AI models

Pre-trained generative AI models are a class of artificial intelligence models that are trained on vast amounts of data to generate new content. These models are designed to learn the underlying patterns and structures of the input data and then use this knowledge to create new, similar content.

They have wide range of applications from NLP and image generation to music composition and beyond.

• GPT (Generative Pre-trained Transformer):
  • Models likeGPT-3 and GPT-4, developed by OpenAI are capable of generating human-like text based on the input they receieve. The ycan perform tasks such as text generation, summarization, translation and question answering.
• BERT (Bidirectional Encoder Representations from Transformers):
  • While primarily used for understanding context in text, BERT's masked language modeling approach can be considered generative in specific fine-tuning scenarios.
• T5(Text-To-Text Transfer Transformer):
  • tIt is versatile generative AI model developed by Google Research. It treats every NLP task as text-to-text problem, allowing it to generate responses such as translation, summarization, question-answering and more.
• GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders):
  • These are image generation models. GANs are used to generate realistic images by training two neural networks in opposition to each other (generator and discriminator).
  • VAEs encode input data into latent space and then decode it back to generate new data.

Advanced LLMs - RAG and fine-tuning

Large Language Models (LLMs) is a type of artificial intelligence model trained on vast corpus of text data to understand and generate human-like language. These models are based on deep learning architectures, typically utilizing transformer models.

Understanding and working with LLMs can open up numerous research opportunities and it is hot-topic in the AI research community with ongoing innovations and improvements.

Advanced language models can be enhanced and customized through techniques like Retrieval-Augmented Generation (RAG) and fine-tuning.

RAG: Combines language models with information retrieval systems to provide more accurate and contextually relevant responses. It works by retrieving relevant documents or data from a large corpus and then generating responses based on this information. Reduces hallucination by providing concrete references for generation.

Fine-tuning: Involves training a pre-trained language model on a specific dataset to adapt it to a particular task or domain. One of the advantage is that, it tailors the model to specific needs, improving performance on domain-specific tasks.

Combining Fine-tuning and RAG:

• Using fine-tuning to adapt a language model to a specific domain, followed by RAG for integrating external knowledge, can yield highly accurate and contextually rich responses.
• Fine-tuning ensures the model understands domain-specific terminology and nuances, while RAG ensures up-to-date and relevant information retrieval.

Example:

• Fine-Tune a Language Model:Fine-tune a GPT or BERT-based model on a domain-specific dataset, such as medical records or legal documents.
• Integrate RAG:Use a retrieval system to pull relevant documents from a comprehensive database or knowledge base.Combine the retrieved documents with the fine-tuned model to generate responses.

Practical applications like - customer support (fine-tune model on past customer support interactions and use RAG to retrieve relevant policy documents or FAQs), research assistant ad legal assistant.

Conclusion

Starting a career in AI can be both exciting and overwhelming, especially with so many concepts to learn. However, by focusing on the fundamental concepts of machine learning and practicing with datasets (example datasets from Kaggle, UCI) one concept at a time, the learning process becomes more manageable. Numerous resources are available online, including YouTube videos, blogs, and articles. Additionally, educational platforms like Udacity, Coursera, AWS and Udemy offer free AI/ML courses, making it easier to build your skills and advance in this field.