Artificial Intelligence

🔗 10 Loss Functions in Machine Learning (and when to use them) Regression Losses 1️⃣ Mean Bias Error (MBE) – Captures average bias in predictions. Rarely used since positive and negative errors cancel out. 2️⃣ Mean Absolute Error (MAE) – Average absolute difference between predicted and actual values. Treats small and large errors equally since gradient magnitude is constant. 3️⃣ Mean Squared Error (MSE) – Squares errors, making large errors count more. Useful, but sensitive to outliers. 4️⃣ Root Mean Squared Error (RMSE) – Square root of MSE. Keeps loss in the same units as the target variable. 5️⃣ Huber Loss – Hybrid of MAE and MSE. Acts like MSE for small errors and MAE for large ones. Needs a hyperparameter to define the transition point. 6️⃣ Log-Cosh Loss – Smooth, non-parametric alternative to Huber. More stable but a bit more computationally expensive. Classification Losses 1️⃣ Binary Cross-Entropy (BCE) – Standard for binary classification. Measures mismatch between predicted probabilities and true labels. 2️⃣ Hinge Loss – Based on the margin between points and decision boundary. Penalizes wrong predictions and low-confidence correct ones. Used in training SVMs. 3️⃣ Cross-Entropy Loss – Generalization of BCE for multi-class classification tasks. 4️⃣ KL Divergence – Measures how one probability distribution diverges from another. For classification, minimizing KL is equivalent to minimizing cross-entropy, but it’s widely used in t-SNE and knowledge distillation.

🚀 Want to speed up training in PyTorch by several times? DataLoader has two bad defaults that slow down the process. By fixing them, I got almost 5x speedup. ❌ Problem - .to(device) transfers data to the GPU. - While the GPU is computing, the CPU does nothing. - While the CPU prepares data, the GPU is idle. ⚡️ Solution You need to make the CPU and GPU work in parallel: - In DataLoader, set pin_memory=True - When transferring data, use .to(device, non_blocking=True) - Add num_workers to DataLoader for background loading. ✅ As a result, the CPU prepares the next batch while the GPU is busy with the current one. This eliminates idle time, and training goes noticeably faster.

🔗 Machine Learning Roadmap

Whether you're just starting out or looking to refine your skills, this Machine Learning Roadmap breaks down every step

1️⃣ Build a solid foundation in math and stats 2️⃣ Dive into ML algorithms like Linear Regression, SVM, and Clustering 3️⃣ Choose your ML focus, from supervised learning to recommender systems 4️⃣ Master popular libraries like PyTorch, TensorFlow, and Scikit-learn 5️⃣ Gain real-world experience with projects and side gigs

🔗 Top Machine Learning Algorithms For Classification

🌟 InfoSeek: data synthesis for deep research with HCSP formalization. BAAI introduced InfoSeek — an open methodology for data synthesis and a training loop for deep research. Tasks of this class go beyond ordinary fact extraction: the model must decompose the question into subtasks, coordinate multi-step reasoning, and verify answers against sources.

These tasks are formalized as HCSP — hierarchical constraint satisfaction problems, whose solution arises only through sequential narrowing of candidates at multiple levels, where each internal node is itself a subtask, and the dependency between nodes forms a research tree.

The basic idea is simple: data is built around a research tree. The nodes are entities or atomic facts, edges are verifiable relations from Wikipedia and open pages. The synthesis algorithm explicitly manages the structure to eliminate underdetermination or early "short circuits." In HCSP, the answer is formally the intersection of sets defined by current constraints and recursive subquestions; in terms of the tree, the root is the final answer. This approach not only sets the depth and breadth of reasoning but also makes each intermediate step verifiable by specific statements. 🟡The synthesis is performed by a pair of agents. The planner controls global complexity by selecting the goal and type of expansion, while the Browser extracts facts and links from the entity page. Four operations cover the entire lifecycle: 🟢Initialization from the "anchor"; 🟢"Parent blurring" — adding several independent conditions that collectively define a unique answer without inclusions among candidates; 🟢Vertical deepening via hyperlink to increase the tree height; 🟢Question text generation only after each node has a sufficient set of verifiable constraints and the specified complexity metrics are met. Quality is controlled along two axes: complexity and verifiability. Initially, questions are run "head-on": if a powerful base model answers correctly without search, the sample is excluded; about 2% were filtered this way. Then solvability is checked on a fixed set of pages with distractor impurities, and all ambiguity is removed. Result: a dataset with 50K question–answer pairs and 16.5K reasoning trajectories with extraction labels. 🟡Experiments. Tests showed that InfoSeek generalizes beyond the home domain. On classic fact extraction and multi-hop question sets, the compact InfoSeeker-3B model outperforms typical RAG and agent pipelines. On BrowseComp-Plus with a fixed corpus of 100K pages and BM25, accuracy reaches 16.5% with an average of 8.24 search calls, which is higher than Gemini 2.5 Flash, Sonnet 4, and GPT-4.1, and significantly higher than Qwen3-32B and Search-R1-32B. Replacing the training set NQ+HQA with InfoSeek raises accuracy from 3.0% to 16.5% and makes queries meaningfully more frequent. ▶️ The project already provides a dataset, technical report, data tree constructor, and code for SFT training. Plans include RL code and publication of InfoSeeker-3B weights. 📌Licensing: Apache 2.0 License. 🟡Dataset 🟡Arxiv 🖥GitHub

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🔗 3 Types of Machine Learning

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📝 New research on text creativity Scientists have shown: texts created by humans are semantically newer than those generated by AI. 🔎 How it was measured They introduced the metric "semantic novelty" — the cosine distance between adjacent sentences. 🧠 Main findings Human texts consistently show higher novelty across different embedding models (RoBERTa, DistilBERT, MPNet, MiniLM). In the "human-AI storytelling" dataset, the human contribution was semantically more diverse. ✨ But there is a nuance What we call AI "hallucinations" can be useful in collaborative storytelling. They add unexpected twists and help maintain interest in the story. 👉 Conclusion: humans are more innovative, AI is more predictable, but together they enhance each other. 🔗 Details

🔗 Cheat sheets for Machine Learning and Data Science interviews

A developer posted a useful set of cheat sheets for interview preparation. They contain all the most important information on key ML and DS topics.

Convenient to review before the interview or to brush up your basics.

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Key Concepts for Machine Learning Interviews 1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests. 2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE. 3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand. 4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees. 5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE). 6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization. 7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking. 8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data. 9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis. 10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods. 11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients. 12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data. 13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment. 14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound. 15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks

AI vs ML vs Deep Learning 🤖 You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not. AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning. ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed. Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc. Think of it this way: AI = Science ML = A chapter in the science Deep Learning = A paragraph in that chapter.

🔟 things to know before diving into AI automation An author from Reddit built over 100 workflows and highlighted the most important lessons: 1. Start with simple scenarios — 10 minutes of benefit is better than 10 hours of complexity. 2. Document the process: screenshots and errors are your portfolio. 3. Learn to work with HTTP requests right away — it opens access to almost everything. 4. Don’t call yourself an "expert," say specifically: "I help businesses save time." 5. Know how to say no: sometimes "no" opens the way to more profitable projects. 6. Always think about errors: APIs crash, data breaks. 7. Share failures — they build more trust than perfect cases. 8. Stable income comes not from setup, but from support and improvements. 9. Networking is half the success. Projects come through colleagues. 10. Automate yourself first: the best argument is your own example. 💡 The main thing: businesses don’t need beautiful workflows, but results — for example, "minus 15 hours of routine per week." 🔗 Full post

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⚡️ 25 AI Tools to Boost Your Productivity in 2025! ◽️ Here is a comprehensive list of the most powerful AI tools for various tasks: from audio and video to research and content creation, with hidden links for each tool: 🎙 Audio Field: 🔹 Lovo Tool – for converting text into natural voices. 🔹 Speechify Tool – for turning written texts into audiobooks. 🔹 Murf Tool – to create professional voiceovers. 🔹 Media.io Tool – for easy audio and video editing and conversion. 🌐 Website Field: 🔹 10Web Tool – to create full websites using AI. 🔹 Durable Tool – to build a website in less than 30 seconds. 🔹 AlliAI Tool – for automatic SEO optimization. 🔹 Subpage Tool – to create smart and fast landing pages. 🎥 Video Field: 🔹 Steve Tool – to create videos from texts. 🔹 Pictory Tool – for automatic video editing from text content. 🔹 Deepbrain Tool – to create human videos from texts. 🔹 Heygen Tool – to generate videos with realistic talking faces. 📊 Presentations: 🔹 Beautiful Tool – to design stunning visual presentations. 🔹 Simplified Tool – to easily create designs and marketing presentations. 🔹 Slidesgo Tool – AI-powered ready and customizable presentation templates. 🔬 Scientific Research Field: 🔹 Paperpal Tool – for reviewing and editing academic papers. 🔹 BetaMonic Tool – to suggest recent papers and research by topic. 🔹 Consensus Tool – to get answers supported by reliable research. 🔹 Perplexity Tool – an instant search engine with accurate, sourced answers. 🔹 You Tool – a smart search engine that aggregates results from diverse sources with an interactive experience. ✍️ Content Creation: 🔹 Lovo Tool – to create professional audio content. 🔹 Writesonic Tool – an intelligent writing assistant to generate articles, posts, and ads. — Choose what suits you and start 2025 with higher intelligence and doubled productivity!

🔟 Top Python Libraries for Language AI Models (LLMs) in 2025 🐍🤖 If you work in AI and natural language processing, these libraries are indispensable! 🏆 1. Hugging Face Transformers Library 🔹 Best for: Pretrained language models, training, and inference. 🔹 Why? Provides easy access to load and run the most popular language models, such as GPT and BERT. 💬 2. LangChain Library 🔹 Best for: Building applications based on language models, like chatbots and interactive AI. 🔹 Why? Offers flexible tools to integrate LLMs with databases and APIs. 🧠 3. SpaCy Library 🔹 Best for: Text analysis, Named Entity Recognition (NER), and syntactic parsing. 🔹 Why? Fast and powerful, ideal for enterprise AI projects. 📖 4. NLTK (Natural Language Toolkit) Library 🔹 Best for: Language analysis, text segmentation, and Part-of-Speech (POS) tagging. 🔹 Why? Contains a rich set of linguistic tools for computational linguistics research. 🔎 5. SentenceTransformers Library 🔹 Best for: Semantic search, sentence similarity measurement, and clustering. 🔹 Why? Based on powerful models like BERT and RoBERTa to extract deep meanings from texts. 🔤 6. FastText Library 🔹 Best for: Word embeddings and text classification. 🔹 Why? Developed by Facebook, known for speed and accuracy in multilingual text classification. 📝 7. Gensim Library 🔹 Best for: Topic modeling and text representation (Word2Vec and Doc2Vec). 🔹 Why? Provides efficient algorithms to extract insights from large text corpora. 🏷 8. Stanza Library 🔹 Best for: Named Entity Recognition (NER) and Part-of-Speech (POS) tagging. 🔹 Why? Developed by Stanford University, it is multilingual and highly accurate. 😃 9. TextBlob Library 🔹 Best for: Sentiment analysis, POS tagging, and text processing. 🔹 Why? Easy to use, suitable for beginners in natural language analysis. 🌍 10. Polyglot Library 🔹 Best for: Multilingual text processing, entity recognition, and word representation. 🔹 Why? Supports over 130 languages, making it ideal for global projects. 🚀 Whether you are a beginner developer or an AI expert, these libraries will help you build the most powerful applications based on language models!

🚀 A fantastic resource for everyone who wants to understand how Qwen3 models work: Qwen3 From Scratch This is a detailed step-by-step guide to running and analyzing Qwen3 models — from 0.6B to 32B — from scratch, directly in PyTorch. 📌 What's inside: — How to load the Qwen3‑0.6B model and pretrained weights — Setting up the tokenizer and generating text — Support for the reasoning version of the model — Tricks to speed up inference: compilation, KV cache, batching 📊 The author also compares Qwen3 with Llama 3: ✔️ Model depth vs width ✔️ Performance on different hardware ✔️ How the 0.6B, 1.7B, 4B, 8B, 32B models behave ⚡️ Perfect if you want to understand how inference, tokenization, and the Qwen3 architecture work — without magic or black boxes. 🖥 Github