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En rask referanseguide for binære tall, konverteringer, addisjon og subtraksjon. Perfekt for studenter og profesjonelle innen datateknikk og elektronikk.

This cheat sheet provides a detailed overview of syndicated loans, including their structure, key players, facility types, and trading aspects. It serves as a quick reference for understanding the complexities of syndicated lending.

A quick reference guide to syndicated loans, covering key concepts, roles, facility types, and processes. Includes a diagram illustrating the structure of a syndicated loan.

A concise guide to the human nervous system, covering its structure, function, and key components like the auditory, visual, and motor systems.

A concise guide to the human nervous system, covering its structure, functions, and key components. This cheat sheet provides a quick reference for students, healthcare professionals, and anyone interested in understanding the complexities of neural communication and control.

A comprehensive guide to Angular component communication, covering various techniques from basic to advanced, including best practices for managing data flow and preventing memory leaks.

A comprehensive TypeScript cheat sheet covering basic to advanced concepts, including types, functions, classes, generics, utility types, and best practices.

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.