# morvanzhou/tutorials

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12,952 stars · 5,683 forks · Python · MIT

## Links

- GitHub: https://github.com/MorvanZhou/tutorials
- Homepage: https://morvanzhou.github.io/tutorials
- awesome-repositories: https://awesome-repositories.com/repository/morvanzhou-tutorials.md

## Topics

`machine-learning` `multiprocessing` `neural-network` `numpy` `python` `sklearn` `tensorflow` `theano` `threading`

## Description

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization.

The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of advanced models such as generative adversarial networks, transformers, and actor-critic agents.

The content also spans broader technical capabilities including automated web data extraction, tabular data manipulation, and the use of multi-processing and multi-threading. Additional material covers the fundamentals of object-oriented programming, version control with Git, and basic Linux system administration.

## Tags

### Artificial Intelligence & ML

- [Machine Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-implementations.md) — Provides code-based implementations of neural networks, reinforcement learning agents, and generative models. ([source](https://cdn.jsdelivr.net/gh/morvanzhou/tutorials@master/README.md))
- [Neural Network Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-implementations.md) — Provides comprehensive implementations of convolutional, recurrent, and autoencoder neural networks. ([source](https://mofanpy.com/tutorials/machine-learning/ML-intro/))
- [Actor-Critic Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/actor-critic-architectures.md) — Implements actor-critic architectures that combine policy-based agents with value-based evaluators for reinforcement learning.
- [Deep Q-Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-q-learning-implementations.md) — Implements reinforcement learning algorithms such as Q-learning and Sarsa for agent training. ([source](https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/))
- [Deep Reinforcement Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-q-learning-implementations/deep-reinforcement-learning-implementations.md) — Implements deep reinforcement learning agents using Policy Gradients and Actor-Critic methods. ([source](https://mofanpy.com/tutorials/machine-learning/ML-intro/))
- [Generative Adversarial Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-adversarial-networks.md) — Provides implementations of generative adversarial networks using competing neural networks to synthesize realistic data.
- [Machine Learning Tutorials](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-tutorials.md) — Provides educational content and practical implementations of neural networks and reinforcement learning algorithms.
- [Machine Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training.md) — Provides utilities to train, fine-tune, and optimize machine learning models using gradient descent. ([source](https://mofanpy.com/tutorials/machine-learning/sklearn/))
- [Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-concepts/network-architectures-and-layers/neural-networks.md) — Constructs various neural network architectures, including regressors and classifiers, for machine learning tasks. ([source](https://mofanpy.com/tutorials/machine-learning))
- [Python Data Science Primers](https://awesome-repositories.com/f/artificial-intelligence-ml/python-data-science-primers.md) — Provides introductory educational resources covering NumPy arrays, Pandas DataFrames, and visualization libraries for data exploration.
- [Reinforcement Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning.md) — Implements reinforcement learning agents that learn optimal behaviors through environmental interaction. ([source](https://mofanpy.com/tutorials/machine-learning/))
- [Reinforcement Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-training.md) — Implements frameworks for training reinforcement learning agents to learn optimal behaviors through rewards. ([source](https://mofanpy.com/tutorials/machine-learning))
- [Sequence-to-Sequence Transformer Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-to-sequence-transformer-architectures.md) — Implements sequence-to-sequence transformer architectures using attention mechanisms for text generation.
- [Attention Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-mechanisms.md) — Implements attention mechanisms to improve the accuracy of language modeling and translation. ([source](https://mofanpy.com/tutorials/machine-learning/nlp/))
- [Evolutionary Algorithms](https://awesome-repositories.com/f/artificial-intelligence-ml/evolutionary-algorithms.md) — Implements optimization techniques based on biological evolution, including genetic algorithms and neuroevolution. ([source](https://mofanpy.com/tutorials/machine-learning/))
- [Experience Replay Buffers](https://awesome-repositories.com/f/artificial-intelligence-ml/experience-replay-buffers.md) — Implements experience replay buffers to store agent transitions and stabilize deep reinforcement learning training.
- [Image-to-Image Translation](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-pipelines/text-to-image-generators/image-inpainting/image-to-image-translation.md) — Provides guides for mapping images from one domain to another using generative models and style transfer. ([source](https://mofanpy.com/tutorials/machine-learning/gan/))
- [Conditional Generative Modeling](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-model-training-tools/conditional-training/conditional-generative-modeling.md) — Implements methods to control generative model outputs by providing specific conditions or styles. ([source](https://mofanpy.com/tutorials/machine-learning/gan/))
- [Hardware Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/gpu-training-accelerators/hardware-acceleration.md) — Explains how to offload heavy mathematical operations to GPUs to reduce neural network training time. ([source](https://mofanpy.com/tutorials/machine-learning/torch/))
- [Neural Network Operations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/architecture-and-operations/neural-network-operations.md) — Implements the mathematical foundations of networks, including matrix multiplication and training logic. ([source](https://mofanpy.com/tutorials/data-manipulation/numpy/))
- [Model Persistence](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/data-and-checkpointing/model-loading/model-persistence.md) — Provides methods for saving trained estimators to disk and reloading them for reuse without retraining. ([source](https://mofanpy.com/tutorials/machine-learning/theano/))
- [Model Generalization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-generalization.md) — Applies Dropout and Batch Normalization to ensure models perform reliably on unseen data. ([source](https://mofanpy.com/tutorials/machine-learning/torch/))
- [Generalization Techniques](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/profiling-and-benchmarking/model-performance-optimization/generalization-techniques.md) — Applies dropout and batch normalization techniques to prevent overfitting and improve model generalization. ([source](https://mofanpy.com/tutorials/machine-learning/tensorflow/))
- [Model Performance Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-evaluators.md) — Provides techniques for validating model accuracy and reliability using evaluation metrics and cross-validation. ([source](https://mofanpy.com/tutorials/machine-learning/sklearn/))
- [Model Training Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-optimizers.md) — Utilizes feature normalization and regularization to optimize training convergence and model performance. ([source](https://mofanpy.com/tutorials/machine-learning/ML-intro/))
- [Model Persistence](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/model-persistence.md) — Implements mechanisms for saving and loading trained model weights and graph structures for reuse. ([source](https://mofanpy.com/tutorials/machine-learning/tensorflow/))
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Implements libraries and techniques for sentiment analysis and translation of human language data. ([source](https://mofanpy.com/tutorials/machine-learning))
- [Gradient Descent Algorithms](https://awesome-repositories.com/f/artificial-intelligence-ml/optimization-algorithms/gradient-descent-algorithms.md) — Implements gradient descent algorithms to optimize model weights and minimize error during training.
- [Pre-training Transfer Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/pre-training-transfer-learning.md) — Demonstrates how to use pre-trained models as starting points for new tasks to optimize training. ([source](https://mofanpy.com/tutorials/machine-learning/tensorflow/))
- [Robotic Arm Training](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-training/robot-policy-trainers/scalable-robot-policy-trainings/robotic-arm-training.md) — Implements reinforcement learning algorithms to develop controllers for robotic arm manipulation tasks. ([source](https://mofanpy.com/tutorials/machine-learning/ML-practice/))
- [Synthetic Data Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generators.md) — Implements generative adversarial networks to create synthetic data samples and images. ([source](https://mofanpy.com/tutorials/machine-learning))

### Education & Learning Resources

- [Programming Fundamentals](https://awesome-repositories.com/f/education-learning-resources/programming-fundamentals.md) — Provides comprehensive instructional materials on core language constructs, syntax, and control flow for beginners. ([source](https://mofanpy.com/tutorials/python-basic/basic/))
- [PyTorch Deep Learning Examples](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education/deep-learning-platforms/pytorch-deep-learning-examples.md) — Provides reference implementations of CNN, RNN, and Transformer architectures using the PyTorch framework.
- [Concurrent Programming Tutorials](https://awesome-repositories.com/f/education-learning-resources/educational-resources/languages-and-programming-concepts/programming-language-mastery-guides/python-tutorials/concurrent-programming-tutorials.md) — Provides specialized lessons on implementing multi-processing and multi-threading to optimize CPU performance in Python.
- [Python Programming Guides](https://awesome-repositories.com/f/education-learning-resources/python-programming-guides.md) — Offers educational resources and tutorials focused on Python language features, syntax, and object-oriented programming.
- [Python Programming Tutorials](https://awesome-repositories.com/f/education-learning-resources/python-programming-tutorials.md) — Provides sequential instructional content to help users master core Python syntax and usage. ([source](https://mofanpy.com/tutorials/))
- [Tabular Data Analysis](https://awesome-repositories.com/f/education-learning-resources/tabular-data-analysis.md) — Provides educational methods for loading, inspecting, and analyzing the structure of tabular datasets. ([source](https://mofanpy.com/tutorials/data-manipulation))
- [Machine Learning Fundamentals](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education/machine-learning-fundamentals.md) — Provides foundational educational content on machine learning workflows and optimization techniques. ([source](https://mofanpy.com/tutorials/machine-learning/))
- [Linux Environment Tutorials](https://awesome-repositories.com/f/education-learning-resources/linux-environment-tutorials.md) — Provides instructional guides on using command line tools to set up stable Linux development environments. ([source](https://cdn.jsdelivr.net/gh/morvanzhou/tutorials@master/README.md))
- [Web Scraping Courses](https://awesome-repositories.com/f/education-learning-resources/python-programming-guides/web-scraping-courses.md) — Offers educational materials and curricula focused on web data extraction using requests and BeautifulSoup.

### Scientific & Mathematical Computing

- [Scientific Computing](https://awesome-repositories.com/f/scientific-mathematical-computing/high-performance-execution-environments/scientific-computing-platforms/scientific-computing.md) — Provides a comprehensive guide to scientific computing, including high-performance numerical operations and array manipulations. ([source](https://mofanpy.com/tutorials/data-manipulation))
- [Parallel Processing](https://awesome-repositories.com/f/scientific-mathematical-computing/high-performance-execution-environments/high-performance-and-parallel-computing/parallel-processing.md) — Implements parallel processing techniques to distribute workloads across multiple cores and improve efficiency. ([source](https://mofanpy.com/tutorials/python-basic/))
- [Multi-Dimensional Arrays](https://awesome-repositories.com/f/scientific-mathematical-computing/multi-dimensional-arrays.md) — Executes mathematical calculations and reshapes multi-dimensional arrays for numerical analysis. ([source](https://mofanpy.com/tutorials/data-manipulation/numpy/))
- [Numerical Array Operations](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-array-operations.md) — Perform mathematical operations, indexing, and merging on multi-dimensional arrays to handle large datasets. ([source](https://mofanpy.com/tutorials/data-manipulation/np-pd/))
- [GPU-Accelerated Computation](https://awesome-repositories.com/f/scientific-mathematical-computing/gpu-accelerated-computation.md) — Provides guides on offloading heavy mathematical operations to GPUs to reduce neural network training and inference time.

### Part of an Awesome List

- [General Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/general-machine-learning.md) — Demonstrates the use of standard libraries to implement supervised, unsupervised, and semi-supervised learning. ([source](https://mofanpy.com/tutorials/machine-learning/))
- [Data Analysis and Visualization](https://awesome-repositories.com/f/awesome-lists/data/data-analysis-and-visualization.md) — Combines scientific computing and visualization tools to manipulate datasets and extract information. ([source](https://cdn.jsdelivr.net/gh/morvanzhou/tutorials@master/README.md))
- [Data Manipulation](https://awesome-repositories.com/f/awesome-lists/data/data-manipulation.md) — Provides tools for cleaning missing values, merging datasets, and exporting tabular data. ([source](https://mofanpy.com/tutorials/data-manipulation/np-pd/))
- [Neural Network Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/neural-network-frameworks.md) — Demonstrates how to build deep learning models using standard frameworks like PyTorch. ([source](https://mofanpy.com/tutorials/machine-learning/))
- [Pre-trained Language Models](https://awesome-repositories.com/f/awesome-lists/ai/pre-trained-language-models.md) — Provides guides on applying pre-trained language models to natural language processing tasks. ([source](https://mofanpy.com/tutorials/machine-learning/nlp/))
- [Text Sequence Generators](https://awesome-repositories.com/f/awesome-lists/ai/sequence-to-sequence-models/text-sequence-generators.md) — Develops sequence-to-sequence models using Transformer and CNN architectures to generate text. ([source](https://mofanpy.com/tutorials/machine-learning/nlp/))
- [File and Directory Management](https://awesome-repositories.com/f/awesome-lists/devtools/file-and-directory-management.md) — Teaches the use of command line tools to manage files and directories. ([source](https://mofanpy.com/tutorials/others/linux-basic/))
- [Linux Installation and Usage](https://awesome-repositories.com/f/awesome-lists/learning/linux-and-system-fundamentals/linux-installation-and-usage.md) — Offers educational materials on the basic operation and setup of Linux distributions for development. ([source](https://mofanpy.com/tutorials/))

### Data & Databases

- [Data Visualization](https://awesome-repositories.com/f/data-databases/data-analysis-visualization/visualization-frameworks-libraries/data-visualization.md) — Uses libraries to render data into visual formats such as scatter, bar, and 3D charts. ([source](https://mofanpy.com/tutorials/data-manipulation/plt/))
- [Data Analysis Workflows](https://awesome-repositories.com/f/data-databases/data-analysis-workflows.md) — Implements end-to-end workflows for cleaning, transforming, and analyzing tabular datasets.
- [Data Cleaning Procedures](https://awesome-repositories.com/f/data-databases/data-cleaning-procedures.md) — Provides methods for filtering and correcting errors in datasets to prepare them for analysis. ([source](https://mofanpy.com/tutorials/data-manipulation/pandas/))
- [Web Data Extraction](https://awesome-repositories.com/f/data-databases/web-data-extraction.md) — Demonstrates how to parse HTML structures using CSS selectors and regular expressions to retrieve data. ([source](https://mofanpy.com/tutorials/data-manipulation/))
- [Animated Data Representations](https://awesome-repositories.com/f/data-databases/animated-data-representations.md) — Creates animated visualizations that show how numerical data evolves over time or iterations. ([source](https://mofanpy.com/tutorials/data-manipulation/plt/))
- [Dataset Merging](https://awesome-repositories.com/f/data-databases/data-deduplication-tools/dataset-merging.md) — Implements capabilities for combining and reorganizing multiple datasets through concatenation and grouping. ([source](https://mofanpy.com/tutorials/data-manipulation/pandas/))
- [Web Data Pipelines](https://awesome-repositories.com/f/data-databases/data-integration-synchronization/event-driven-data-pipelines/web-data-pipelines.md) — Implements automated workflows to extract, transform, and load data from web-based sources. ([source](https://mofanpy.com/tutorials/python-basic/interactive-python/))
- [Scraping Acceleration](https://awesome-repositories.com/f/data-databases/scraping-acceleration.md) — Increases data retrieval speeds using multi-processing distributed systems and asynchronous loading. ([source](https://mofanpy.com/tutorials/data-manipulation/scraping/))
- [Semantic Word Embeddings](https://awesome-repositories.com/f/data-databases/vector-storage/text-vectorizers/semantic-word-embeddings.md) — Implements word-to-vector conversions using CBOW and Skip-Gram to capture semantic meanings in text. ([source](https://mofanpy.com/tutorials/machine-learning/nlp/))

### Development Tools & Productivity

- [Concurrent Task Execution](https://awesome-repositories.com/f/development-tools-productivity/concurrent-task-execution.md) — Executes multiple operations simultaneously using concurrency models to handle several tasks without sequential blocking. ([source](https://mofanpy.com/tutorials/python-basic/))
- [Parallel Task Execution](https://awesome-repositories.com/f/development-tools-productivity/parallel-task-execution.md) — Implements parallel task execution to run multiple operations across different CPU cores simultaneously. ([source](https://mofanpy.com/tutorials/python-basic/multiprocessing/))
- [Thread-Safe Communication Channels](https://awesome-repositories.com/f/development-tools-productivity/thread-managers/thread-safe-communication-channels.md) — Provides thread-safe communication channels for passing information between concurrent execution units. ([source](https://mofanpy.com/tutorials/python-basic/threading/))
- [Version Control](https://awesome-repositories.com/f/development-tools-productivity/developer-utilities-libraries/developer-tools/version-control-ecosystems/version-control.md) — Teaches how to track code changes and manage project history using version control systems. ([source](https://cdn.jsdelivr.net/gh/morvanzhou/tutorials@master/README.md))
- [Git Versioning Managers](https://awesome-repositories.com/f/development-tools-productivity/git-versioning-managers.md) — Provides practical guides on using Git to manage code versions and project history. ([source](https://mofanpy.com/tutorials/))
- [Grid Plot Arrangements](https://awesome-repositories.com/f/development-tools-productivity/notebook-plot-rendering/multi-format-plot-renderers/grid-plot-arrangements.md) — Arranges multiple plots in grid layouts to enable side-by-side comparison of datasets. ([source](https://mofanpy.com/tutorials/data-manipulation/plt/))

### Operating Systems & Systems Programming

- [Inter-Process Communication](https://awesome-repositories.com/f/operating-systems-systems-programming/inter-process-communication.md) — Implements inter-process communication using thread-safe queues to synchronize state and share data between processes.
- [Multi-Process Task Distribution](https://awesome-repositories.com/f/operating-systems-systems-programming/multi-process-task-distribution.md) — Demonstrates how to distribute workloads across multiple CPU cores using worker pools to bypass execution locks.

### Programming Languages & Runtimes

- [Language Data Structures](https://awesome-repositories.com/f/programming-languages-runtimes/language-data-structures.md) — Provides guides on storing and manipulating data using core Python lists, tuples, dictionaries, and sets. ([source](https://mofanpy.com/tutorials/python-basic/basic/))
- [Thread-Synchronized Execution](https://awesome-repositories.com/f/programming-languages-runtimes/language-features-paradigms/concurrency-models/concurrency/execution-models/multi-threaded-execution/thread-synchronized-execution.md) — Coordinates the timing of multiple threads using synchronization barriers to ensure tasks complete in order. ([source](https://mofanpy.com/tutorials/python-basic/threading/))
- [Parallel Computing Implementation](https://awesome-repositories.com/f/programming-languages-runtimes/parallel-computing-implementation.md) — Demonstrates strategies for scaling computational throughput across multiple CPU cores using multi-processing.

### Software Engineering & Architecture

- [Object-Oriented Programming](https://awesome-repositories.com/f/software-engineering-architecture/architectural-design-patterns/object-oriented-foundations/object-oriented-programming.md) — Teaches the programming paradigm of organizing software around objects, classes, and encapsulation. ([source](https://mofanpy.com/tutorials/python-basic/basic/))
- [Shared Memory Management](https://awesome-repositories.com/f/software-engineering-architecture/shared-memory-management.md) — Manages shared memory and synchronization primitives to coordinate state between concurrent processes. ([source](https://mofanpy.com/tutorials/python-basic/multiprocessing/))
- [Process Pools](https://awesome-repositories.com/f/software-engineering-architecture/task-scheduling/parallel-task-executors/parallel-task-spawning/process-pools.md) — Uses process pools to manage collections of worker processes for executing independent functions in parallel. ([source](https://mofanpy.com/tutorials/python-basic/multiprocessing/))
- [Code Version Trackings](https://awesome-repositories.com/f/software-engineering-architecture/code-version-trackings.md) — Explains how to track file changes and organize project history to enable state reversion. ([source](https://mofanpy.com/tutorials/others))
- [CPU Optimization Strategies](https://awesome-repositories.com/f/software-engineering-architecture/performance-reliability/performance-optimization/computational-efficiency/cpu-optimization-strategies.md) — Offers strategies for distributing tasks across multiple CPU cores to increase computational efficiency. ([source](https://mofanpy.com/tutorials/python-basic))
- [Race Condition Prevention](https://awesome-repositories.com/f/software-engineering-architecture/race-condition-prevention.md) — Demonstrates how to lock shared resources during access to ensure thread-safe data modification. ([source](https://mofanpy.com/tutorials/python-basic/threading/))

### System Administration & Monitoring

- [Linux System Administration](https://awesome-repositories.com/f/system-administration-monitoring/administrative-operations/linux-system-administration.md) — Provides a comprehensive guide to managing, configuring, and maintaining Linux-based systems. ([source](https://mofanpy.com/tutorials/others))

### Testing & Quality Assurance

- [Error Handling](https://awesome-repositories.com/f/testing-quality-assurance/debugging-diagnostics/error-handling.md) — Provides instructional content on catching runtime exceptions and verifying code through unit testing. ([source](https://mofanpy.com/tutorials/python-basic/interactive-python/))

### Web Development

- [Web Scraping and Automation](https://awesome-repositories.com/f/web-development/web-automation-scraping/web-scraping-automation.md) — Implements systems for automating browser interactions and crawling web content to extract structured datasets.
- [Web Scrapers](https://awesome-repositories.com/f/web-development/web-automation-scraping/web-scraping-automation/web-scraping/web-scrapers.md) — Provides practical implementations for building automated systems to navigate websites and extract structured data. ([source](https://cdn.jsdelivr.net/gh/morvanzhou/tutorials@master/README.md))
- [Browser Automation](https://awesome-repositories.com/f/web-development/web-automation-scraping/web-scraping-automation/browser-automation.md) — Provides guides on using browser automation frameworks to scrape dynamic web content. ([source](https://mofanpy.com/tutorials/data-manipulation/scraping/))

### Graphics & Multimedia

- [Numerical Statistics Analyzers](https://awesome-repositories.com/f/graphics-multimedia/media-processing-analysis/media-analysis/automated-media-analyzers/library-statistics-analyzers/numerical-statistics-analyzers.md) — Calculates descriptive statistics and numerical metrics across datasets to summarize trends. ([source](https://mofanpy.com/tutorials/data-manipulation/pandas/))

### Networking & Communication

- [SSH Client Connections](https://awesome-repositories.com/f/networking-communication/ssh-client-connections.md) — Guides users through establishing secure remote terminal sessions using SSH. ([source](https://mofanpy.com/tutorials/others/linux-basic/))