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Train a Model Faster with torch.compile and Gradient Accumulation
This article is divided into two parts; they are: • Using `torch. source https://machinelearningmastery.com/train-a-m…
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Training a Model with Limited Memory using Mixed Precision and Gradient Checkpointing
This article is divided into three parts; they are: • Floating-point Numbers • Automatic Mixed Precision Training • Gra…
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Practical Agentic Coding with Google Jules
If you have an interest in agentic coding, there's a pretty good chance you've heard of https://machinelearning…
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Evaluating Perplexity on Language Models
This article is divided into two parts; they are: • What Is Perplexity and How to Compute It • Evaluate the Perplexity …
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Train Your Large Model on Multiple GPUs with Pipeline Parallelism
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How LLMs Choose Their Words: A Practical Walk-Through of Logits, Softmax and Sampling
This article is divided into four parts; they are: • How Logits Become Probabilities • Temperature • Top- k Sampling • …
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Mastering JSON Prompting for LLMs
LLMs https://machinelearningmastery.com/mastering-json-prompting-for-llms/
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Essential Chunking Techniques for Building Better LLM Applications
Every large language model (LLM) application that retrieves information faces a simple problem: how do you break down…
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5 Advanced Feature Engineering Techniques with LLMs for Tabular Data
In the epoch of LLMs, it may seem like the most classical machine learning concepts, methods, and techniques like featu…
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7 Feature Engineering Tricks for Text Data
An increasing number of AI and machine learning-based systems feed on text data — language models are a notable example…
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Build an Inference Cache to Save Costs in High-Traffic LLM Apps
Large language models (LLMs) are widely used in applications like chatbots, customer support, code assistants, and more…
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Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data
Imbalanced datasets are a common challenge in machine learning. source https://machinelearningmastery.com/algorithm-s…
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MinMax vs Standard vs Robust Scaler: Which One Wins for Skewed Data?
You've loaded your dataset and the distribution plots look rough. source https://machinelearningmastery.com/minma…
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7 Python Decorator Tricks to Write Cleaner Code
Usually shrouded in mystery at first glance, Python decorators are, at their core, functions wrapped around other funct…
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A Gentle Introduction to Batch Normalization
Deep neural networks have drastically evolved over the years, overcoming common challenges that arise when training the…
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3 Ways to Speed Up and Improve Your XGBoost Models
Extreme gradient boosting ( XGBoost ) is one of the most prominent machine learning techniques used not only for experi…
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7 Pandas Tricks for Efficient Data Merging
Data merging is the process of combining data from different sources into a unified dataset. source https://machinele…
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How to Decide Between Random Forests and Gradient Boosting
When working with machine learning on structured data, two algorithms often rise to the top of the shortlist: random fo…
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A Gentle Introduction to Bayesian Regression
In this article, you will learn: • The fundamental difference between traditional regression, which uses single fixed v…
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10 Useful NumPy One-Liners for Time Series Analysis
Working with time series data often means wrestling with the same patterns over and over: calculating moving averages, …
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Logistic vs SVM vs Random Forest: Which One Wins for Small Datasets?
When you have a small dataset, choosing the right machine learning model can make a big difference. source https://ma…
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5 Scikit-learn Pipeline Tricks to Supercharge Your Workflow
Perhaps one of the most underrated yet powerful features that scikit-learn has to offer, pipelines are a great ally for…
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7 Pandas Tricks to Improve Your Machine Learning Model Development
If you're reading this, it's likely that you are already aware that the performance of a machine learning model…
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A Practical Guide to Handling Out-of-Memory Data in Python
These days, it is not uncommon to come across datasets that are too large to fit into random access memory (RAM), espec…
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How to Diagnose Why Your Classification Model Fails
In classification models , failure occurs when the model assigns the wrong class to a new data observation; that is, wh…
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7 Matplotlib Tricks to Better Visualize Your Machine Learning Models
Visualizing model performance is an essential piece of the machine learning workflow puzzle. source https://machinele…
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How to Interpret Your XGBoost Model: A Practical Guide to Feature Importance
One of the most widespread machine learning techniques is XGBoost (Extreme Gradient Boosting). source https://machine…
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Time-Series Transformation Toolkit: Feature Engineering for Predictive Analytics
In time series analysis and forecasting , transforming data is often necessary to uncover underlying patterns, stabiliz…
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A Gentle Introduction to Q-Learning
Reinforcement learning is a relatively lesser-known area of artificial intelligence (AI) compared to highly popular sub…
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Your First Containerized Machine Learning Deployment with Docker and FastAPI
Deploying machine learning models can seem complex, but modern tools can streamline the process. source https://machi…
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Word Embeddings for Tabular Data Feature Engineering
It would be difficult to argue that word embeddings — dense vector representations of words — have not dramatically rev…
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Decision Trees Aren’t Just for Tabular Data
Versatile, interpretable, and effective for a variety of use cases, decision trees have been among the most well-establ…
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Skip Connections in Transformer Models
This post is divided into three parts; they are: • Why Skip Connections are Needed in Transformers • Implementation of …
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Unlocking Performance: Accelerating Pandas Operations with Polars
https://machinelearningmastery.com/unlocking-performance-accelerating-pandas-operations-with-polars/
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Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker
You've trained your machine learning model, and it's performing great on test data. source https://machinelea…
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NumPy Ninjutsu: Mastering Array Operations for High-Performance Machine Learning
Machine learning workflows typically involve plenty of numerical computations in the form of mathematical and algebraic…
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Tokenizers in Language Models
This post is divided into five parts; they are: • Naive Tokenization • Stemming and Lemmatization • Byte-Pair Encoding …
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Selecting the Right Feature Engineering Strategy: A Decision Tree Approach
In machine learning model development, feature engineering plays a crucial role since real-world data often comes with …
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A Gentle Introduction to Learning Rate Schedulers
Ever wondered why your neural network seems to get stuck during training, or why it starts strong but fails to reach it…
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Custom Fine-Tuning for Domain-Specific LLMs
Fine-tuning a large language model (LLM) is the process of taking a pre-trained model — usually a vast one like GPT or …
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How to Combine Pandas, NumPy, and Scikit-learn Seamlessly
Machine learning workflows require several distinct steps — from loading and preparing data to creating and evaluating …
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Automate Dataset Labeling with Active Learning
A few years ago, training AI models required massive amounts of labeled data. source https://machinelearningmastery.c…
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PyTorch Lightning Hyperparameter Optimization with Optuna
https://machinelearningmastery.com/pytorch-lightning-hyperparameter-optimization-with-optuna/
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Building RAG Systems with Transformers
This post is divided into five parts: • Understanding the RAG architecture • Building the Document Indexing System • Im…
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Let’s Build a RAG-Powered Research Paper Assistant
In the era of generative AI, people have relied on LLM products such as ChatGPT to help with tasks. source https://ma…
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Understanding Text Generation Parameters in Transformers
This post is divided into seven parts; they are: - Core Text Generation Parameters - Experimenting with Temperature - T…
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Building a RAG Pipeline with llama.cpp in Python
Using llama. source https://machinelearningmastery.com/building-a-rag-pipeline-with-llama-cpp-in-python/
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Generating and Visualizing Context Vectors in Transformers
This post is divided into three parts; they are: • Understanding Context Vectors • Visualizing Context Vectors from Dif…
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5 Lessons Learned Building RAG Systems
Retrieval augmented generation (RAG) is one of 2025's hot topics in the AI landscape. source https://machinelearn…
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Understanding RAG Part X: RAG Pipelines in Production
Be sure to check out the previous articles in this series: • https://machinelearningmastery.com/understanding-rag-part-…
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A Gentle Introduction to Graph Neural Networks in Python
Graph neural networks (GNNs) can be pictured as a special class of neural network models where data are structured as g…
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10 Must-Know Python Libraries for LLMs in 2025
Large language models (LLMs) are changing the way we think about AI. source https://machinelearningmastery.com/10-mus…
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Implementing Multilingual Translation with T5 and Transformers
This post is divided into three parts; they are: • Setting up the translation pipeline • Translation with alternatives …
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Building Q&A Systems with DistilBERT and Transformers
This post is in three parts; they are: • Building a simple Q&A system • Handling Large Contexts • Building an Exper…
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Debugging PyTorch Machine Learning Models: A Step-by-Step Guide
Debugging machine learning models entails inspecting, discovering, and fixing possible errors in the internal mechanism…
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A Gentle Introduction to Transformers Library
The transformers library is a Python library that provides a unified interface for working with different transformer m…
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Statistical Methods for Evaluating LLM Performance
The large language model (LLM) has become a cornerstone of many AI applications. source https://machinelearningmaster…
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Diagnosing and Fixing Overfitting in Machine Learning with Python
Overfitting is one of the most (if not the most!) common problems encountered when building machine learning (ML) model…
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Auto-Completion Style Text Generation with GPT-2 Model
This post is in six parts; they are: • Traditional vs Neural Approaches • Auto-Complete Architecture • Basic Auto-Compl…
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Understanding RAG Part VI: Effective Retrieval Optimization
Be sure to check out the previous articles in this series: • https://machinelearningmastery.com/understanding-rag-part-…
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Understanding RAG Part V: Managing Context Length
Be sure to check out the previous articles in this series: • https://machinelearningmastery.com/understanding-rag-part-…
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Next-Level Data Science (7-Day Mini-Course)
Before we start, let's ensure you are in the right place. source https://machinelearningmastery.com/next-level-da…
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Creating Custom Layers and Loss Functions in PyTorch
Creating custom layers and loss functions in https://machinelearningmastery.com/creating-custom-layers-loss-functions-p…
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The Role of Domain Knowledge in Machine Learning: Why Subject Matter Experts Matter
Machine learning (ML) is considered the largest subarea of artificial intelligence (AI) , studying the development of s…
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A Practical Guide to the Claude API
The API for https://machinelearningmastery.com/practical-guide-claude-api/
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RAG Hallucination Detection Techniques
Large language models (LLMs) are useful for many applications, including question answering, translation, summarization…
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5 Common Mistakes to Avoid When Training LLMs
Training large language models (LLMs) is an involved process that requires planning, computational resources, and domai…
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