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The Practical AI Digest

Mo Bhuiyan via NotebookLM
Machine Learning
Artificial Intelligence
Generative Adversarial Networks
Graph Neural Networks
AI and Machine Learning
Image Generation
Agentic AI
Impact-E Framework
Loss Functions
Data Preparation
Data Structures
Large Language Models
Marketing Mix Modeling
Hyperparameters
Reinforcement Learning
AI Applications
Tool Use
Chip Design
Navigation Systems
Natural Language Processing

Distilling AI/ML theory into practical insights. One concept at a time. No jargon.

PublishesTwice monthlyEpisodes13Founded9 months ago
Number of ListenersCategory
Technology

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Latest Episodes

This episode explores multimodal AI : models that can see, read, and even hear. We explain how models like OpenAI’s CLIP learn joint representations of images and text (by matching pictures with their captions), enabling capabilities like image capti... more

This episode dives into strategies for fine-tuning gigantic AI models without needing massive compute. We explain parameter-efficient fine-tuning methods like LoRA (Low-Rank Adaptation), which freezes the original model and trains only small adapter ... more

In this episode, we break down what diffusion models are and why they’ve become the go-to method for AI image generation. You’ll learn how these models gradually add and remove noise to transform random pixels into coherent images, enabling use cases... more

This episode breaks down what graph neural networks (GNNs) are and why they matter. You’ll learn how GNNs use nodes and edges to represent relationships and how message passing lets models make sense of social, biological, and networked data. We’ll a... more

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How Embeddings and Vector Databases Power Generative AI
Q: How do we extract information from video content effectively?
The extraction process starts with automatic speech recognition to transcribe audio and then key visual frames are identified for further processing.
How Embeddings and Vector Databases Power Generative AI
Q: Let's talk about Retrieval Augmented Generation, or RAG.
RAG is crucial for allowing AI models to pull in current specific information rather than just relying on prior training.

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Frequently Asked Questions About The Practical AI Digest

What is The Practical AI Digest about and what kind of topics does it cover?

Content focuses on the intersection of artificial intelligence (AI) and machine learning (ML) with practical insights that can be applied in various industries. Topics commonly addressed include Graph Neural Networks, neuro-symbolic AI, large language model applications in chip design, and advanced AI simulation methods like Markov Chain Monte Carlo. This collection of episodes is structured to minimize jargon, making complex concepts accessible to a broader audience. Unique aspects include the emphasis on real-world applications, the role of AI in managing uncertainty, and the integration of advanced AI technologies into business practices, highlighting its transformative potential in sectors like healthcare, finance, and marketing.

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1. Data Science Interview Prep

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The Practical AI Digest launched 9 months ago and published 13 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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