Generative AI: Foundation Models and Platforms is the eighth course offered as part of a 10-course Professional certificate provided by IBM – IBM AI Product Manager Professional Certificate in Coursera. This course continues to discuss in-depth GenAI technologies. This is probably the only course in this specialization that is technical in nature. It covers a breadth of deep learning concepts and theories. This is also a beginner-level course organized over three modules including video lectures, opinion discussions with practicing professionals, assignments, quizzes, and a final exam.

Module 1 – Models for Generative AI, focuses on the foundational models used in generative AI, including deep learning, large language models (LLMs), GANs, VAEs, transformers, and diffusion models. It also introduces foundation models and how they can be used to generate content.

Module 2 – Platforms for Generative AI dives into pre-trained models and platforms used for generative AI development. You’ll learn how to use pre-trained models to generate text, images, and code. The functionalities and applications of different platforms like IBM watsonx and Hugging Face are also covered.

Module 3 is just a conclusion and final quiz.

Study Notes from the Course

Deep Learning Process

Deep learning is inspired by the human brain’s structure and functions. It involves creating depth through layers of information processing. The deeper the layers, the more sophisticated the understanding, akin to human cognition. This is achieved using Artificial Neural Networks (ANNs), which consist of interconnected computing units known as neurons. These neurons are organized into three primary layers:

1. Input Layer: Captures the initial data.
2. Hidden Layers: One or more layers where data is analyzed.
3. Output Layer: Produces the final result.

Training Data Types

Supervised Learning

In supervised learning, algorithms are trained on labeled datasets where each input has a known output. This method is used for applications like email filtering, credit scoring, fraud detection, and image and voice recognition. While labeled data improves the quality of the trained algorithms, it is often time-consuming and costly to obtain, and the algorithms are limited to predefined responses.

Unsupervised Learning

Unsupervised learning deals with unlabeled data, allowing algorithms to identify patterns and hierarchies without predefined outputs. Applications include clustering, where similar data points are grouped based on inherent properties, and dimensionality reduction, which identifies the most important data features while discarding redundant information. This approach enables more flexible and efficient discovery of patterns within datasets, often leading to more accurate results.

Text-to-text generation models

Text-to-text generation models are a type of machine learning model designed to generate text based on a given input. These models can perform tasks such as summarizing text, translating languages, creating content, and more. They are trained on large text corpora to understand and learn patterns, grammar, and contextual information. The generated text can range from code and scripts to musical pieces, emails, and letters.

Types of Models: There are two main types of text-to-text generation models: statistical models and neural network models.

1. Statistical Models:
   • These models use statistical techniques to generate text.
   • An example is the Markov chain, which generates text by starting with a seed state and predicting the next state based on the previous one. Markov chains are used for tasks like speech recognition and journalism.
2. Neural Network Models:
   • These models use artificial neural networks to represent complex relationships between data.
   • They can be further categorized into sequence-to-sequence models and transformer models.
   • Sequence-to-sequence models encode the input text into a sequence of numbers and then decode it into new text. They are used for tasks like summarization, speech recognition, and machine translation.
   • Transformer models map input text directly to generated text using an attention mechanism to emphasize related words, resulting in more fluent and natural-sounding text.

Popular Models – GPT by OpenAI, T5 (Text-to-Text Transfer Transformer) by Google AI, BART (Bidirectional Autoregressive Transformer) by Facebook AI

Neural Network Architectures

Different architectures are employed in deep learning, each suited to specific types of data and tasks:

1. Convolutional Neural Networks (CNNs):
   • Structure: Comprise a series of layers performing convolutions (mathematical operations) on previous layers.
   • Applications: Extract useful information from grid-based data like images, useful in image processing, video recognition, and natural language processing.
   • Use Cases: Image classification, pattern recognition, and picture segmentation.
2. Recurrent Neural Networks (RNNs):
   • Structure: Efficient at processing sequential data due to their memory component that captures dependencies over time.
   • Applications: Machine translation, sentiment analysis, and speech recognition.
   • Use Cases: Text and speech data processing, recognizing contextual information over time.
3. Transformer-based Models:
   • Structure: Utilize an encoder-decoder architecture without convolutions or recurrence, handling exceptionally high numbers of parameters.
   • Applications: Natural language processing tasks like content generation, predictive analysis, and language translation.
   • Use Cases: Creating content, dialogue systems, and translating languages.

Large Language Models (LLMs)

Large Language Models (LLMs) like OpenAI’s GPT-3 and GPT-4, Google’s PALM 2, and Meta’s Llama are built on transformer networks. These models are trained on vast corpora of text data from the internet, including books, articles, and websites. For instance, GPT-4, with over 170 trillion parameters, performs a wide range of natural language processing tasks such as:

• Content Generation: Writing essays and case papers.
• Dialogue Systems: Powering chatbots and virtual agents.
• Language Translation: Translating international business communications and web content into local languages.

Foundation Models

Large AI models trained on massive amounts of data that can be adapted for various tasks. They excel at understanding and generating text, images, and other forms of data.

Key Points

• Training: Foundation models are trained on vast datasets using self-supervised learning.
• Capabilities: They can perform tasks like answering questions, writing essays, translating languages, generating images, and writing code.
• Examples: Well-known examples include OpenAI’s GPT-3 and GPT-4, Google’s BERT and PaLM, and Meta’s AI.
• Adaptability: These models can be fine-tuned for specific applications, making them versatile and cost-effective.
• Limitations: Potential biases in training data and hallucinations (generating false information) are challenges.

Overall, foundation models represent a significant advancement in AI, offering powerful tools for various industries but requiring careful use and evaluation.

Generative AI Models

The primary generative AI models are Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformer-based, and Diffusion models. Each model employs unique deep-learning architectures and probabilistic techniques to achieve its goals. These models are used in various industries, each with specific strengths and challenges. GANs, for instance, require significant data and computational power, while VAEs are versatile with smaller datasets.

The key learnings/notes about each of these models are provided below

• Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs)
• Transformer-based Models
• Diffusion Models

The course also quickly introduces Hugging Face.

Overall a very leaded course introducing different types of models, theoretical intordiuctions to Generative AI. A lot to digest and learn. But the course doesn’t go into details of any models; the discussions are kept at introductory and a user level.

I liked going through the course, quick and easy intro to all jargons and models. This course also give access to WatsonX that gave exercises to try out the concepts in a step-by-step fashion.

Highly recommend taking this course!