"Learn Generative AI with David Foster's book"

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 is not an isolated concept—it’s the next step in the evolution of AI. From AI → Machine Learning → Deep Learning → Generative AI, each layer builds on the previous to enable machines that can now generate text, images, code, and more. The journey is just beginning, and the impact across industries will be transformative. #ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI #Innovation

The Gatekeepers of Deep Learning: Deciding What Lives and Dies In the silent depths of a deep learning model, information flows layer by layer, growing sharper, wiser, more refined. But not everything survives the journey. At every neuron stands a Gatekeeper, cold, impartial, unyielding. Their ancient names echo through the architectures of AI: Sigmoid, Tanh, ReLU. These activation functions decide what lives and what dies, which signals pass forward and which are suppressed into silence. Without them, the network would be lifeless, linear, predictable, incapable of understanding complexity. But when the Gatekeepers fail, when they saturate or die, the horror begins. The neurons start to lie. The model begins to hallucinate, seeing patterns that never existed. These silent sentinels, coded in mere lines of math, shape the very truth your AI believes. So the next time you train a model, remember respect the Gatekeepers. They are the ones deciding what lives and dies in your deep learning model.

🧠 Day 2 (12 Weeks) – The Intelligence Stack Today's session mapped how AI branches into Machine Learning, Deep Learning, and now Generative AI. At the core lies Machine Learning — where systems learn from data: • Supervised: learns toward a target • Unsupervised: groups data without one • Reinforcement: improves through rewards Every leap in GenAI stands on these foundations — machines that first learned, then learned to learn. #KapilLearnsAI #GenAI #AIinBanking #MachineLearning #DeepLearning 🔍 How would you explain the difference between ML and DL to a newcomer?

Thanks to the rise of AI tools, terms like machine learning, deep learning, and generative AI get thrown around a lot. And to really understand how LLMs and other tools work, you should know what they mean. In this guide, Nitheesh discusses the differences between these three terms/fields along with their applications. https://lnkd.in/g3uN3kUC

Exploring more into the world of Artificial Intelligence has been an eye-opening experience. Recently, I focused on understanding the difference between Discriminative AI and Generative AI, along with how Machine Learning and Deep Learning approach data differently. 🔹 Discriminative AI models focus on classifying data for example, identifying whether an email is spam or not. 🔹 Generative AI, learns patterns deeply enough to create new data such as text, images. I also revisited how Deep Learning, with its neural networks and hidden layers, goes beyond traditional Machine Learning by handling vast, unstructured, or unlabeled datasets more efficiently. It’s fascinating to see how these evolving AI approaches are influencing everything from automation to intelligent decision-making systems areas that directly intersect with modern software quality and testing. #ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI #TechInsights #ContinuousLearning

In compliance, we’re often asked to weigh in on AI without everyone sharing the same definition. That’s where misunderstandings start. This 10-minute video from IBM is one of the clearest overviews I’ve seen on the difference between Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI. For anyone new to the topic, or looking for a refresher, it is a good starting point. 👉 https://lnkd.in/eCuFdwSe

Exploring AI and Machine Learning has become so much smoother with the Comet browser by Perplexity I’ve been using Comet to dive deep into AI and ML concepts — from understanding how neural networks work to exploring real-world applications of generative AI. What makes Comet stand out for me is how it turns research into an interactive, reliable experience with real-time answers and trusted sources. One of my favorite features is how Comet helps me download extractable, formatted PDFs — making it super easy to save, organize, and reference information without losing structure or readability. It’s a game-changer for research and productivity. Whether I’m learning, writing, or brainstorming ideas, Comet keeps my workflow seamless and efficient. Download using this link and write your first prompt : https://lnkd.in/g8VbW5AJ 🧠✨ #AI #MachineLearning #CometBrowser #PerplexityAI #Productivity #Research #TechTools

Getting the definitions straight! 🧠 In this video from IBM Technology, Martin Keen clearly explains the difference between AI, Machine Learning, and Deep Learning, and how they fit together in the tech hierarchy. A must-watch for anyone looking to understand the fundamentals. #AI #MachineLearning #DeepLearning #DataScience #TechExplained https://lnkd.in/eBdVKw2Y

The perceptron is the foundational building block of neural networks — a simple yet powerful binary classifier that transforms how machines learn. It works by taking multiple inputs, assigning weights, adding a bias, and applying an activation function to decide whether to activate or not. Think of it as a smart, yes/no decision maker that lays the groundwork for complex AI. This minimalist model paved the way for modern machine learning, demonstrating how intelligent systems can learn from data through weighted decisions. A true cornerstone for anyone exploring AI, machine learning, or data science. #Perceptron #MachineLearning #AI #NeuralNetworks #SupervisedLearning #DataScience #FutureTech #AIFoundation