#deeplearning

Deep Learning is a popular field of machine learning that focuses on neural networks and advanced architectures to learn highly complex representations from data that standard ML techniques struggle to learn. Some common tools, areas of study, and models used in deep learning are: Neural networks and optimization, the building blocks of deep learning. Sequence modeling to process ordered data: - Recurrent neural networks - LSTMs and gating - Sequence-to-sequence models - Attention - Transformers - Language models Computer vision: - Convolutional neural networks - Image classification - Object detection - Image segmentation - Video models Geometric learning to represent structured data: - Graph neural networks - 3D vision - Geometric representations Generative modeling to learn and produce new data: - Autoencoders and VAEs - GANs - Diffusion models - Autoregressive models Modern foundation architectures emerging from the transformer architecture: - Large language models - Vision transformers - Vision-language models - Multimodal models - Foundation models Keep up with AI research with our weekly visual newsletter. Join 10,000+ other AI engineers, researchers and PMs getting clear weekly AI breakdowns (link in bio). #deeplearning #machinelearning #computerscience #datascience #math

🚀 AI Engineer Roadmap 2026 Becoming an AI Engineer isn’t about learning ChatGPT overnight. It’s about climbing one step at a time: 🐍 Python 📊 Data Analysis 🗄️ SQL 🤖 Machine Learning 🧠 Deep Learning 💬 NLP 👁️ Computer Vision 🤗 Transformers 🔗 RAG ⚡ AI Agents ☁️ MLOps 🚀 Production AI Systems The people building tomorrow’s AI products are mastering today’s fundamentals. Which step are you currently on? 👇 #AIEngineer #ArtificialIntelligence #MachineLearning #DeepLearning #Python DataScience LLM GenerativeAI AIAgents RAG MLOps TechCareer Programming LearnAI CodeOnPaper

Sometimes, an AI model learns the answer long after it looks like it already knows it. 🧠 Researchers discovered a surprising phenomenon called grokking while training neural networks. A model could reach nearly perfect performance on its training data while continuing to perform poorly on new, unseen examples. Then, after many additional training steps, its performance on unseen data could suddenly improve dramatically. This delayed jump in generalization challenged the usual assumption that models start understanding a task as soon as they fit their training data. Instead, grokking suggests that neural networks can spend a long period memorizing examples before eventually reorganizing their internal representations and discovering a more general pattern. The result is a fascinating shift from memorization to generalization, showing that what happens inside a neural network during late-stage training can be very different from what its training accuracy suggests. It also raises a bigger question about AI: sometimes, a model may appear to be learning nothing new when it is actually developing a deeper representation of the problem. Credit: Welch Labs #ai #machinelearning #deeplearning #neuralnetworks #technology

Chaos vs order. Can a neural network learn a function log(x) sin(1/x)? �Web: maksymzubkov.info | YouTube: @MathForLife #machinelearning #neuralnetworks #deeplearning #mathematics #ai

The Dragon Hatchling (BDH) is a post-transformer architecture inspired by the structure and dynamics of the human brain. Instead of relying on dense self-attention, BDH represents computation as a sparse graph of neuron-like nodes connected by weighted synapses that encode learned implications between concepts. The model reasons through repeated cycles of local computation, where active nodes propagate evidence to neighboring nodes, synaptic connections are strengthened through Hebbian learning, and only the strongest activations survive through competitive selection. Unlike transformers, whose weights remain fixed during inference, BDH continuously updates its synaptic state as it processes new information, enabling continual learning and dynamic memory formation. This brain-inspired architecture offers linear-time inference, bounded memory usage, and recurrent reasoning. Pathway believes it a promising alternative for long-context reasoning and future AI systems that must continuously learn and adapt. We write about AI research weekly, including the BDH. You can check it out in our bio. Join 9000+ Others learning AI Visually. Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #deeplearning #computerscience #datascience #coding

AI, ML, Deep Learning, GenAI, LLMs, RAG, and Agentic AI are connected, but they aren't the same. Understanding how they fit together helps you choose the right technology for the right problem. This infographic breaks down each concept, shows their relationship, highlights real-world use cases, and explains why they matter in modern AI development. Save this guide for quick revision and share it with someone learning AI. #AI #ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI LLM RAG AgenticAI AIAgents PromptEngineering

comment “molecule” and I’ll send you the dataset #deeplearning #research #ai #drugdiscovery #research

Anyi ngantuk di jam terakhir 😪😴 #english #teachedudigital #deeplearning #guru

He raised $1B to prove ChatGPT is wrong. $1.03 BILLION raised with zero products and zero revenue. Why? Because the smartest minds in tech think the entire AI industry is heading down a dead end. Yann LeCun’s thesis is simple: real intelligence doesn’t start with language. It starts in the physical world. While everyone else doubles down on LLMs, AMI Labs is building the next generation of AI. #AI #yannlechun #deeplearning #metaai #siliconvalley

the “tiny squish” is called an activation function. If it didn’t exist, ChatGPT would be one matrix. #tech #engineering #artificalintelligence #artificialintelligence #deeplearning

Comment “AI” and I’ll send you all 40 AI mastery websites straight to your DM. If you want to learn AI in 2026 without jumping between random resources, this list gives you a clear path across: → Python → SQL → Math & Statistics → Machine Learning → Git & GitHub → APIs & Backend → Deep Learning → LLMs & Generative AI [AI Engineering, Machine Learning, Python, SQL, Deep Learning, LLMs, Generative AI, AI Resources] #AIEngineering #MachineLearning #GenerativeAI #DeepLearning #ArtificialIntelligence

Data Science is a popular field that combines statistical reasoning, computational methods, and machine learning to solve business problems using data. Some common tools, areas of study, and models used in data science are: - Data prep and cleaning to correct missing, inconsistent, or noisy information - Exploratory analysis and visualization to uncover distributions, relationships, trends, and anomalies within datasets. - Database querying retrieve and organize information efficiently. Statistical inference through: - probability - hypothesis testing - confidence intervals - and experimentation to quantify uncertainty and validate conclusions. Predictive modeling to estimate outcomes using methods such as: - Regression - Classification - Decision trees - Ensemble learning - Support vector methods Unsupervised learning to identify underlying patterns and simplify data using techniques like: - Clustering - Dimensionality reduction - Temporal modeling and forecasting to analyze trends and dependencies in sequential data. - Personalization, ranking (recommendation) to surface relevant information - Anomaly detection systems to identify rare or abnormal behavior. Together, these methods form a framework for converting data into evidence, reliable predictions, and grounded decision-making. Learn AI concepts, Visually. Join 8000+ Others in our Visually Explained Deep Learning Newsletter. Get your weekly AI breakdown (link in bio). #deeplearning #machinelearning #computerscience #datascience #math