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A quick reference guide covering essential kinematics concepts, formulas, and graphs.

A comprehensive cheat sheet covering various machine learning algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning, along with deep learning architectures.

✅ 1. Supervised Learning • Regression o Linear Regression o Logistic Regression o Polynomial Regression o Ridge Regression o Lasso Regression o ElasticNet o Support Vector Machines (SVM) o Decision Trees o Random Forest • Classification o Logistic Regression o K-Nearest Neighbors (KNN) o Support Vector Machines (SVM) o Decision Trees o Random Forest o Naive Bayes o Confusion Matrix o Stochastic Gradient Descent o Gradient Boosting o AdaBoost o XGBoost o LightGBM o CatBoost ________________________________________ 🔍 2. Unsupervised Learning • Clustering 🔹 1. Centroid-Based Clustering • K-Means • K-Medoids • Mean-Shift ________________________________________ 🔹 2. Density-Based Clustering • DBSCAN • OPTICS • HDBSCAN ________________________________________ 🔹 3. Hierarchical Clustering • Agglomerative Clustering • BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) • Affinity Propagation ________________________________________ 🔹 4. Distribution-Based Clustering • Gaussian Mixture Models (GMM) • Dimensionality Reduction o PCA (Principal Component Analysis) o t-SNE o UMAP o ICA (Independent Component Analysis) o LDA (Linear Discriminant Analysis) ________________________________________ 🔁 3. Semi-Supervised Learning • Self-Training • Label Propagation • Label Spreading ________________________________________ 🔄 4. Reinforcement Learning • Q-Learning • Deep Q-Networks (DQN) • SARSA • Policy Gradient Methods • Actor-Critic • Proximal Policy Optimization (PPO) • Deep Deterministic Policy Gradient (DDPG) ________________________________________ 🧠 5. Deep Learning Algorithms 🔹 1. Feedforward Networks (FNN) • Multilayer Perceptron (MLP) • Deep Neural Networks (DNN) ________________________________________ 🔹 2. Convolutional Neural Networks (CNN) • LeNet • AlexNet • VGGNet • GoogLeNet (Inception) • ResNet • DenseNet • EfficientNet • MobileNet • SqueezeNet ________________________________________ 🔹 3. Recurrent Neural Networks (RNN) • Vanilla RNN • Long Short-Term Memory (LSTM) • Gated Recurrent Unit (GRU) • Bidirectional RNN • Deep RNNs • Echo State Networks (ESN) ________________________________________ 🔹 4. Attention-Based Models / Transformers • Transformer • BERT • GPT (GPT-1, GPT-2, GPT-3, GPT-4) • RoBERTa • ALBERT • XLNet • T5 • DistilBERT • Vision Transformer (ViT) • Swin Transformer • DeiT • Performer • Longformer ________________________________________ 🔹 5. Autoencoders • Vanilla Autoencoder • Sparse Autoencoder • Denoising Autoencoder • Contractive Autoencoder • Variational Autoencoder (VAE) ________________________________________ 🔹 6. Generative Adversarial Networks (GANs) • Vanilla GAN • Deep Convolutional GAN (DCGAN) • Conditional GAN (cGAN) • CycleGAN • StyleGAN • Pix2Pix • BigGAN • StarGAN • WGAN (Wasserstein GAN) • WGAN-GP ________________________________________ 🔹 7. Reinforcement Learning (Deep RL) • Deep Q-Network (DQN) • Double DQN • Dueling DQN • Policy Gradient • REINFORCE • Actor-Critic • A3C (Asynchronous Advantage Actor-Critic) • PPO (Proximal Policy Optimization) • DDPG (Deep Deterministic Policy Gradient) • TD3 (Twin Delayed DDPG) • SAC (Soft Actor-Critic)

A comprehensive cheat sheet covering core machine learning algorithms, evaluation metrics, and essential concepts for interview preparation. Includes supervised, unsupervised learning, deep learning and NLP.

A concise cheat sheet outlining the key concepts, algorithms, and differences between supervised and unsupervised learning methods in machine learning.

List of useful tips & tricks that I'm collecting

This cheatsheet provides a comprehensive quick reference to Hotwire's core components in Ruby on Rails, including Turbo, Stimulus, and their integration for building fast, dynamic web applications. It covers essential setup steps, code examples, debugging techniques, and best practices to streamline development and improve performance.

A comprehensive cheat sheet covering the key aspects of Early Elizabethan England, from Queen Elizabeth's government and religious policies to the challenges she faced at home and abroad, and the vibrant Elizabethan society during the Age of Exploration.

A comprehensive cheat sheet for Visual Studio, covering essential shortcuts, features, and tools for efficient development.

A comprehensive cheat sheet for macOS Finder, covering essential keyboard shortcuts and actions to boost your file management efficiency.

A comprehensive cheat sheet for using Rsync, covering essential options, examples, and use cases for efficient file synchronization and backup.

A comprehensive cheat sheet for Nmap, covering essential scanning techniques, options, and usage examples for network discovery and security auditing.