216
SHThe salary is a side effect. Learning is the real job.
#llm #softwareengineer
@shashank.techandlife
#Nuralnetwork
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156
TENLP - Word embedding
#startupindia
#datascience
#machinelearning
#artificialintelligence
#softwareengineer
@techno.notes
7.7K
PRThis is a clip from our weekly learning sessions for the entire engineering team - where we learn specific advance topics in Artificial Intelligence.
(AI Training Logic, Cross-Entropy Loss, AdamW Optimizer, Weight Decay, Model Overfitting, Neural Network Parameters, Machine Learning Convergence, Loss Minimization, Epochs and Iterations, AI Research 2026, Deep Learning Workflows, Gradient Descent, Mathematical Optimization,360 Labs, Custom Software, AI-Native, Enterprise AI, SaaS is Dead, Software Development 2026, Technical Sovereignty, Product Engineering, Deep Tech India,Lightyear, Prithvi Raj Agrawal, Saurabh Kumar, AI agents, AI services ,AI software development, growth-driven startups, engineering team, AI product development)
@prithvirajagarawal
1.5K
IL#artificialintelligence #machinelearning #neuralnetworks #datascience #systemdesign techfacts ai softwareengineering
One emerging direction in AI research is World Models — systems that learn an internal representation of how environments evolve over time.
Instead of mapping input → output directly, these models learn a latent representation of the world and simulate future states.
This allows an AI system to predict what might happen next before acting.
Some important research in this area includes:
🔹 Dreamer (DeepMind)
A reinforcement learning algorithm that learns a latent world model and plans actions inside it.
🔹 Genie (Google DeepMind)
A model that can generate interactive virtual environments from video.
🔹 Neural Radiance Fields (NeRF)
A method developed by NVIDIA researchers that reconstructs 3D scenes from images.
🔹 World Models (Ha & Schmidhuber, 2018)
One of the early papers proposing that AI agents could train inside simulated environments.
Why this matters:
World models allow AI systems to plan, imagine, and simulate outcomes before acting, similar to how humans mentally simulate scenarios.
This could dramatically improve:
• robotics training
• autonomous systems
• game AI
• generative video models
• embodied AI
The long-term idea is simple but powerful:
AI might learn not just from the world…
but from simulated worlds it creates itself.
@ila.in.progress
173
AICheck the link in bio to read about AI concepts, courses, and more questions like this.
Why must activation functions in neural networks be non-linear?
If every layer were linear, what would happen to the network’s learning power?
Comment A, B, C, or D before checking the answer 👇
Train like an AI engineer.
#ArtificialIntelligence #MachineLearning #NeuralNetworks #DeepLearning #AIQuiz
Ace AI
@ai.myth.buster
1.0K
GOAI is not replacing software engineers anytime soon. They will likely only empower them to do more. Moreover, the need for abundant, novel data to train models and human-created, high-fidelity content means we'll likely need them indefinitely. #AISoftware #SoftwareEngineering #FutureOfTech #AIMeetups #TechTrends #Programming #AIModels #DeepLearning
@gojutechtalk
2.2K
TEAI engineering 2026 cheat sheet
https://www.letsinterview.me/blog/ai-engineer-jobs-india-2026
#aiengineering #programming #job #2026 #aiengineer
@terminally_rooted
![#Nuralnetwork Reel by @techuila_ - Website : https://nn.rocks
[neural networks, ml, ai, software, tech, techuila]](https://image.pictame.com/img/651542454_17902363140388462_8623472679766865973_n.jpg?hash=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)
3.0K
TEWebsite : https://nn.rocks
[neural networks, ml, ai, software, tech, techuila]
@techuila_
4.7K
REA neural network visualizer that shows how an MLP learns step by step. Runs in the browser, trained with PyTorch, and works best on desktop
#coding #programming #softwaredevelopment #computerscience #cse
@real_kingsleymayor
1.7K
SATake your time learning and don’t rush it🔥
#softwareengineering
@sayed.developer
11.7K
MADrowning in ML algorithms? 😵💫 Choosing the right model, feature engineering, hyperparameter tuning... it’s a marathon with no finish line in sight!
Enter AutoGluon: your game-changer for simplified, high-performance machine learning. With just three lines of code, it automates model selection, training, and even handles messy data like a pro! Say goodbye to endless trial-and-error.
Want to revolutionize your ML workflow? Drop a comment ‘REPO’ below, and I’ll send you the official AutoGluon GitHub repository link to get started! 👇
#AutoML #MachineLearning #DataScience #AutoGluon #MLOps Python AI TechCareer SimplifyingML DeveloperTools FutureOfAI Coding AI2025 MLCommunity
@madhav_somani
19.7K
LEMost AI engineers focus on models…
But production doesn’t fail there.
It fails in infrastructure.
Scaling mistakes, missing alerts, silent failures —
that’s what actually takes systems down.
If you’re building AI apps, start understanding what runs behind them.
Save this for later. Share it with someone building AI.
#AIEngineer #DevOps #Kubernetes #MLOps #infrastructure
@learnwithdevopsengineer
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