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🎙️ Welcome back to The Rest is AI!
Ever wondered what the difference is between Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI? These buzzwords are everywhere—but most people still confuse them.
Today, I break down each of these concepts in simple, relatable terms—with real-world examples and analogies that actually make sense.
👉 By the end, you'll know:
1. What AI really meansHow Machine Learning works (and what makes it different)
2. Why Deep Learning feels like sci-fi?
3. What’s behind the magic of Generative AI tools like ChatGPT and MidJourney
💼 Find my latest AI courses on LinkedIn: https://www.linkedin.com/in/shreyas768/
🌐 Website: https://www.shreyshukla.com/
🎵 Listen on YouTube Music: Click Here
Watch Video on YouTube
🎙️ Welcome back to The Rest is AI!
Ever wondered what the difference is between Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI? These buzzwords are everywhere—but most people still confuse them.
Today, I break down each of these concepts in simple, relatable terms—with real-world examples and analogies that actually make sense.
👉 By the end, you'll know:
1. What AI really meansHow Machine Learning works (and what makes it different)
2. Why Deep Learning feels like sci-fi?
3. What’s behind the magic of Generative AI tools like ChatGPT and MidJourney
💼 Find my latest AI courses on LinkedIn: https://www.linkedin.com/in/shreyas768/
🌐 Website: https://www.shreyshukla.com/
🎵 Listen on YouTube Music: Click Here
Watch Video on YouTube
Buckle up as I take you on an exciting journey into the world of ground truth data—the foundation that powers artificial intelligence to make sense of the world! In this video, we unpack everything you need to know about ground truth, from its critical role in training AI models to its real-world impact on technologies you interact with daily, like self-driving cars, voice assistants, and medical diagnostics. 🚗🗣️💉What exactly is ground truth, and why is it the gold standard for teaching AI to "think" like humans? We’ll explain how this carefully curated, real-world data acts as the ultimate reference point for machines. By the end, you’ll not only understand why ground truth is the unsung hero of the AI revolution.✅ Don’t forget to like, comment, and subscribe for more AI and machine learning insights.🎙️ From The Rest is AI – by Shreyas Shukla
💼 Find my latest AI courses on LinkedIn: https://www.linkedin.com/in/shreyas768/
🌐 Website: https://www.shreyshukla.com/
🎵 Listen on YouTube Music: Click Here
Watch Video on YouTube : https://youtu.be/Kd5Wswjab9w
How do neural networks actually learn? The answer lies in Backpropagation — a foundational algorithm that allows AI systems to improve and adapt.In this video, we’ll break down:1. What is Backpropagation in simple terms2. Key concepts like weights, biases, activation functions & forward propagation3. The role of the loss function and gradient descent4. Static vs. recurrent backpropagation5. Real-world applications in OCR, spam detection, sentiment analysis & more✅ Don’t forget to like, comment, and subscribe for more AI and machine learning insights.🎙️ From The Rest is AI – by Shreyas Shukla💼 Find my latest AI courses on LinkedIn: https://www.linkedin.com/in/shreyas768/🌐 Website: https://www.shreyshukla.com/🎵 Audiobooks on YouTube Music: Click Here#Backpropagation #NeuralNetworks #AIExplained #DeepLearning #MachineLearning #TheRestIsAI #GradientDescent #ArtificialIntelligence #TechForBeginners #MLAlgorithms #RecurrentNeuralNetworks #AIEducation
🎧 Watch Video on YouTube : https://www.youtube.com/@shreyas768
As AI continues to shape decisions in hiring, finance, healthcare, and beyond, there's a growing concern about algorithmic bias—when machines learn from flawed data and end up making unfair or discriminatory decisions.
In this episode, we break down:
What algorithmic bias really means
How biased training data, flawed design, and proxy attributes contribute
Shocking real-world examples—from biased resume screening to discriminatory mortgage rates
Actionable strategies to reduce bias in AI systems
We’ll also explore the importance of diverse datasets, human oversight, and inclusive development teams in building fair, transparent, and responsible AI.
🌐 Website: https://www.shreyshukla.com/
💼 LinkedIn: https://www.linkedin.com/in/shreyas768/
🎧 Audiobooks on Spotify: https://open.spotify.com/show/1dUzmPWk2Ua9Q0uDCJpR3U
🎵 Audiobooks on YouTube Music: https://music.youtube.com/channel/UCxsa4SRe-CsQFDa09XHy9cg?si=9rT2A1C6o1o4etBa
Large Language Models (LLMs) are powerful but not perfect! They can be incredibly accurate—or totally wrong. In this episode, we explore Retrieval-Augmented Generation (RAG), a method that helps AI stay up-to-date and provide more reliable answers. 🧠💡📌 What you'll learn:✅ Why LLMs sometimes give incorrect responses✅ How retrieval-augmented models improve accuracy✅ The role of external knowledge sources in AI responsesIf you're curious about AI and how it evolves, this episode is for you! To know more about me and my content on AI :🔗 Connect with me on LinkedIn: https://www.linkedin.com/in/shreyas768/
🌐 Visit my website: https://www.shreyshukla.com/
