NEW BOT Телеграм, страница

I am not really a fan of Cursor, but they definitely got the Marketing team 👏

Please open Telegram to view this post

💯8

808 views18:19

✅ 25 AI & Machine Learning Abbreviations You Should Know 🤖🧠

1️⃣ AI – Artificial Intelligence: The big umbrella for machines mimicking human smarts, from chatbots to self-driving cars.

2️⃣ ML – Machine Learning: AI subset where models learn from data without explicit programming—think predictive analytics.

3️⃣ DL – Deep Learning: ML using multi-layered neural nets for complex tasks like image recognition.

4️⃣ NLP – Natural Language Processing: Handling human language for chatbots or sentiment analysis.

5️⃣ CV – Computer Vision: AI that "sees" and interprets visuals, powering facial recognition.

6️⃣ ANN – Artificial Neural Network: Brain-inspired structures for pattern detection in data.

7️⃣ CNN – Convolutional Neural Network: DL for images/videos, excels at feature extraction like edges in photos.

8️⃣ RNN – Recurrent Neural Network: Handles sequences like time series or text, remembering past inputs.

9️⃣ GAN – Generative Adversarial Network: Two nets competing to create realistic data, like fake images.

🔟 RL – Reinforcement Learning: Agents learn via rewards/punishments, used in games like AlphaGo.

1️⃣1️⃣ SVM – Support Vector Machine: Classification algo drawing hyperplanes to separate data classes.

1️⃣2️⃣ KNN – K-Nearest Neighbors: Simple ML for grouping based on closest data points—lazy learner!

1️⃣3️⃣ PCA – Principal Component Analysis: Dimensionality reduction to simplify datasets without losing info.

1️⃣4️⃣ API – Application Programming Interface: Bridges software, like calling OpenAI's models in your app.

1️⃣5️⃣ GPU – Graphics Processing Unit: Hardware accelerating parallel computations for training big models.

1️⃣6️⃣ TPU – Tensor Processing Unit: Google's custom chips optimized for tensor ops in DL.

1️⃣7️⃣ IoT – Internet of Things: Networked devices collecting data, feeding into AI for smart homes.

1️⃣8️⃣ BERT – Bidirectional Encoder Representations from Transformers: Google's NLP model understanding context both ways.

1️⃣9️⃣ LSTM – Long Short-Term Memory: RNN variant fixing vanishing gradients for long sequences.

2️⃣0️⃣ ASR – Automatic Speech Recognition: Converts voice to text, like Siri or trannoscription tools.

2️⃣1️⃣ OCR – Optical Character Recognition: Extracts text from images, e.g., scanning docs.

2️⃣2️⃣ Q-Learning – Q-Learning: A model-free RL algorithm estimating action values for optimal decisions.

2️⃣3️⃣ MLP – Multilayer Perceptron: Feedforward ANN with hidden layers for non-linear problems.

2️⃣4️⃣ LLM – Large Language Model: Massive text-trained nets like GPT for generating human-like responses (swapped the repeat API for this essential one!).

2️⃣5️⃣ TF-IDF – Term Frequency-Inverse Document Frequency: Scores word importance in text docs for search/retrieval.

@datawithsimon

❤15

954 views05:02