Artificial Intelligence

>> Artificial Intelligence Agents Explained

>> How Does Face Recognition Work?

>> Software Engineering vs. Data Science

>> Deterministic vs. Stochastic Policies in Reinforcement Learning

>> Introduction to Large Language Models

>> What is and Why Use Temperature in Softmax?

>> How Do Self-Organizing Maps Work?

>> Optical Flow: Lucas-Kanade Method

>> Algorithm for Online Outlier Detection in Time Series

• Probability and Statistics

>> What is Gradient Orientation and Gradient Magnitude?

>> How to Analyze Loss vs. Epoch Graphs?

>> The Concepts of Dense and Sparse in the Context of Neural Networks

>> What Are Contours in Computer Vision?

>> What’s a Non-trainable Parameter?

>> What Is the Purpose of a Feature Map in a Convolutional Neural Network

>> What Is Human-Machine Integration?

>> How Does a Neural Network Recognize Images?

>> 2D Convolution as a Matrix-Matrix Multiplication

>> Lazy vs. Eager Learning

>> Classification of Data Mining Systems

>> How Do Artificial Immune Systems Work?

>> DBSCAN Clustering: How Does It Work?

>> Epoch or Episode: Understanding Terms in Deep Reinforcement Learning

>> Q-Learning vs. Deep Q-Learning vs. Deep Q-Network

>> Introduction to Landmark Detection

>> What Are Downstream Tasks?

>> Image Recognition: One-Shot Learning

>> What Is Neural Style Transfer?

>> Automated Machine Learning Explained

>> Image Processing: Graph-based Segmentation

>> What Is Federated Learning?

>> Understanding Otsu’s Method for Image Segmentation

>> What Does Learning Rate Warm-up Mean?

>> What Are Expert Systems?

>> What Is End-to-End Deep Learning?

>> Natural Language Processing: Bleu Score

>> Introduction to Triplet Loss

>> How Do Siamese Networks Work in Image Recognition?

>> Neural Networks: Pooling Layers

>> How Do Eigenfaces Work?

>> Dimensionality of Word Embeddings

>> What Is the Credit Assignment Problem?

>> ADAM Optimizer

>> What Does PAC Learning Theory Really Mean?

>> Image Processing: Sampling and Quantization

>> Computer Vision: Determining the Distance From an Object in a Video

>> Co-occurrence Matrices and Their Uses in NLP

>> Sensitivity and Specificity

>> Hard vs. Soft Voting Classifiers

>> Deep Neural Networks: Padding

>> Difference Between Fundamental Matrix and Essential Matrix

>> Single Shot Detectors (SSDs)

>> What Is State Space Search?

>> Difference Between Reinforcement Learning and Optimal Control

>> What Is Independent Component Analysis (ICA)?

>> Differences Between a Parametric and Non-parametric Model

>> What Is Maxout in a Neural Network?

>> Maximum Likelihood Estimation

• Probability and Statistics

>> Recurrent Neural Networks

>> Graph Attention Networks

>> Sparse Coding Neural Networks

>> Differences Between Luong Attention and Bahdanau Attention

>> Artificial Bee Colony

>> What Are Embedding Layers in Neural Networks?

>> Differences Between Hinge Loss and Logistic Loss

>> Machine Learning: How to Format Images for Training

>> Computer Vision: Differences Between Low-Level and High-Level Features

>> Machine Learning: Flexible and Inflexible Models

>> What Is Space Carving?

>> VAE Vs. GAN For Image Generation

>> Differences Between AR, VR, MR, and XR

>> Difference Between Goal-based and Utility-based Agents

>> What Are Restricted Boltzmann Machines?

>> What Is a Data Lake?

>> What Is Multi-Task Learning?

>> Introduction to Gibbs Sampling

• Probability and Statistics

>> One-Hot Encoding Explained

>> Introduction to Inception Networks

>> Feature Selection in Machine Learning

>> Cognitive Computing vs. Artificial Intelligence

>> Translation Invariance and Equivariance in Computer Vision

>> Neural Network and Deep Belief Network

>> Differences Between Transfer Learning and Meta-Learning

>> Residual Networks

>> Why Use a Surrogate Loss

>> Introduction to Optical Flow

>> Generative Adversarial Networks: Discriminator’s Loss and Generator’s Loss

>> Fast R-CNN: What is the Purpose of the ROI Layers?

>> Parameters vs. Hyperparameters

>> What Is Swarm Intelligence?

>> What Is the No Free Lunch Theorem?

>> How Does Particle Swarm Optimization Work?

>> An Introduction to Computer Vision

>> How Do Blurs in Images Work?

>> What Does Backbone Mean in Neural Networks?

>> The Viola-Jones Algorithm

>> How Does Pose Estimation Work?

>> Spatial Pyramid Pooling

>> Object Detection: SSD Vs. YOLO

>> Understanding Activation Functions

>> What Is Content-Based Image Retrieval?

>> Differences Between Computer Vision and Image Processing

>> Simultaneous Localization and Mapping

>> How Does AI Play Chess?

• Gameplay Algorithms

>> What Are Channels in Convolutional Networks?

>> What Is a Feature Descriptor in Image Processing?

>> Differences Between Bias and Error

>> How Does Optical Character Recognition Work

>> Computer Vision: Stereo 3D Vision

>> What Are Image Histograms?

>> Computer Vision: Popular Datasets

>> Instance Segmentation vs. Semantic Segmentation

>> How to Handle Large Images to Train CNNs?

>> Object Recognition Tasks and Their Differences

>> What Is “Energy” in Image Processing?

>> Neurons in Neural Networks

>> Online Learning vs. Offline Learning

>> What Is One Class SVM and How Does It Work?

>> What Is Feature Importance in Machine Learning?

>> Data Augmentation

>> Attention Mechanism in the Transformers Model

>> What Is Fine-Tuning in Neural Networks?

>> Random Forest vs. Extremely Randomized Trees

>> How to Use Gabor Filters to Generate Features for Machine Learning

>> Random Sample Consensus Explained

>> What Does Pre-training a Neural Network Mean?

>> Neural Networks: What Is Weight Decay Loss?

>> Win Gomoku with Threat Space Search

• Gameplay Algorithms

>> Neural Networks: Difference Between Conv and FC Layers

>> Neural Networks: Binary vs. Discrete vs. Continuous Inputs

>> Differences Between Gradient, Stochastic and Mini Batch Gradient Descent

>> Scale-Invariant Feature Transform

>> What Is a Regressor?

>> What Exactly Is an N-Gram?

>> Hidden Markov Models vs. Conditional Random Fields

>> The Curse of Dimensionality

>> Multi-Layer Perceptron vs. Deep Neural Network

>> Machine Learning: What Is Ablation Study?

>> Silhouette Plots

>> F-Beta Score

>> What Is YOLO Algorithm?

>> Node Impurity in Decision Trees

>> Model-free vs. Model-based Reinforcement Learning

>> 0-1 Loss Function Explained

>> Is a Markov Chain the Same as a Finite State Machine?

• Markov Chains

>> Differences Between Epoch, Batch, and Mini-batch

>> Differences Between Missing Data and Sparse Data

>> Differences Between Backpropagation and Feedforward Networks

>> Disparity Map in Stereo Vision

>> Cross-Validation: K-Fold vs. Leave-One-Out

>> Off-policy vs. On-policy Reinforcement Learning

>> Bias Update in Neural Network Backpropagation

>> Algorithm for Handwriting Recognition

>> Precision vs. Average Precision

>> Comparing Naïve Bayes and SVM for Text Classification

>> Recurrent vs. Recursive Neural Networks in Natural Language Processing

>> Intersection Over Union for Object Detection

>> What Are “Bottlenecks” in Neural Networks?

>> Convolutional Neural Network vs. Regular Neural Network

>> Differences Between Strong-AI and Weak-AI

>> Mean Average Precision in Object Detection

>> Hidden Layers in a Neural Network

>> Transfer Learning vs Domain Adaptation

>> Real-Life Examples of Supervised Learning and Unsupervised Learning

>> Real-World Uses for Genetic Algorithms

>> What Is Depth in a Convolutional Neural Network?

>> What Is the Difference Between Markov Chains and Hidden Markov Models?

• Markov Chains

>> The Reparameterization Trick in Variational Autoencoders

>> Decision Trees vs. Random Forests

>> What Is Inductive Bias in Machine Learning?

>> What Is the Difference Between Artificial Intelligence, Machine Learning, Statistics, and Data Mining?

• Probability and Statistics

>> An Introduction to Self-Supervised Learning

>> Latent Space in Deep Learning

>> Autoencoders Explained

>> Difference Between the Cost, Loss, and the Objective Function

>> Activation Functions: Sigmoid vs Tanh

>> An Introduction to Contrastive Learning

>> Gradient Boosting Trees vs. Random Forests

>> Basic Concepts of Machine Learning

>> Intuition Behind Kernels in Machine Learning

>> Algorithms for Image Comparison

>> Training and Validation Loss in Deep Learning

>> Image Processing: Occlusions

>> Information Gain in Machine Learning

>> Cross-Validation and Decision Trees

>> How to Use K-Fold Cross-Validation in a Neural Network?

>> Applications of Generative Models

>> Calculate the Output Size of a Convolutional Layer

>> Introduction to Curve Fitting

>> An Introduction to Generative Adversarial Networks

>> K-Means for Classification

>> ML: Train, Validate, and Test

>> An Introduction to the Hidden Markov Model

>> Q-Learning vs. SARSA

>> Differences Between SGD and Backpropagation

>> Linearly Separable Data in Neural Networks

>> The Effects of the Depth and Number of Trees in a Random Forest

• Probability and Statistics

>> Using GANs for Data Augmentation

>> Differences Between Bidirectional and Unidirectional LSTM

>> Features, Parameters and Classes in Machine Learning

>> Relation Between Learning Rate and Batch Size

>> Word2vec Word Embedding Operations: Add, Concatenate or Average Word Vectors?

>> Drift, Anomaly, and Novelty in Machine Learning

>> Markov Decision Process: How Does Value Iteration Work?

>> What Is Selection Bias and How Can We Prevent It?

>> Ensemble Learning

>> Decision Tree vs. Naive Bayes Classifier

>> Accuracy vs AUC in Machine Learning

>> Bayesian Networks

>> Biases in Machine Learning

>> When Coherence Score Is Good or Bad in Topic Modeling?

>> How Do Markov Chain Chatbots Work?

• Markov Chains

>> Stratified Sampling in Machine Learning

>> Outlier Detection and Handling

• Probability and Statistics

>> Data Mining in WEKA

>> Choosing a Learning Rate

>> Underfitting and Overfitting in Machine Learning

>> How Do “20 Questions” AI Algorithms Work?

>> How to Calculate the Regularization Parameter in Linear Regression

>> How to Get Vector for A Sentence From Word2vec of Tokens

>> Q-Learning vs. Dynamic Programming

>> Feature Selection and Reduction for Text Classification

>> How Many Principal Components to Take in PCA?

>> Difference Between a SVM and a Perceptron

>> Why Mini-Batch Size Is Better Than One Single “Batch” With All Training Data

>> How to Design Deep Convolutional Neural Networks?

>> Intuitive Explanation of the Expectation-Maximization (EM) Technique

• Probability and Statistics

>> Pattern Recognition in Time Series

>> How to Create a Smart Chatbot?

>> Open-Source AI Engines

>> NLP’s word2vec: Negative Sampling Explained

• Probability and Statistics

>> Out-of-bag Error in Random Forests

>> How to Calculate Receptive Field Size in CNN

>> k-Nearest Neighbors and High Dimensional Data

>> Using a Hard Margin vs. Soft Margin in SVM

>> Value Iteration vs. Policy Iteration in Reinforcement Learning

>> How To Convert a Text Sequence to a Vector

>> Instance vs Batch Normalization

>> Trade-offs Between Accuracy and the Number of Support Vectors in SVMs

>> Open Source Neural Network Libraries

>> Transformer Text Embeddings

>> Generative vs. Discriminative Algorithms

>> Semantic Similarity of Two Phrases

>> Why Feature Scaling in SVM?

>> Automatic Keyword and Keyphrase Extraction

>> Normalization vs Standardization in Linear Regression

>> Word Embeddings: CBOW vs Skip-Gram

>> String Similarity Metrics: Sequence Based

>> How to Improve Naive Bayes Classification Performance?

• Probability and Statistics

>> Ugly Duckling Theorem

>> Topic Modeling with Word2Vec

>> Normalize Features of a Table

>> String Similarity Metrics: Token Methods

>> Gradient Descent Equation in Logistic Regression

>> Correlated Features and Classification Accuracy

• Probability and Statistics

>> Weakly Supervised Learning

>> Interpretation of Loss and Accuracy for a Machine Learning Model

>> Splitting a Dataset into Train and Test Sets

>> Encoder-Decoder Models for Natural Language Processing

>> Solving the K-Armed Bandit Problem

>> Epoch in Neural Networks

>> Epsilon-Greedy Q-learning

>> Random Initialization of Weights in a Neural Network

>> Topic Modeling with Latent Dirichlet Allocation

>> String Similarity Metrics – Edit Distance

>> Batch Normalization in Convolutional Neural Networks

>> Multiclass Classification Using Support Vector Machines

>> Converting a Word to a Vector

>> Reinforcement Learning with Neural Network

>> Converting a Uniform Distribution to a Normal Distribution

>> Gradient Descent vs. Newton’s Gradient Descent

>> How Does the Google “Did You Mean?” Algorithm Work?

>> What Is Cross-Entropy?

>> Algorithms for Determining Text Sentiment

>> Advantages and Disadvantages of Neural Networks Against SVMs

>> Top-N Accuracy Metrics

>> Sentiment Analysis Dictionaries

>> Neural Network Architecture: Criteria for Choosing the Number and Size of Hidden Layers

>> Training Data for Sentiment Analysis

>> Differences Between Classification and Clustering

>> F-1 Score for Multi-Class Classification

>> The Difference Between Epoch and Iteration in Neural Networks

>> What Is a Policy in Reinforcement Learning?

>> What Is the Difference Between Gradient Descent and Gradient Ascent?

>> SVM Vs Neural Network

>> Support Vector Machines (SVM)

>> Levenshtein Distance Computation

>> Normalizing Inputs for an Artificial Neural Network

>> What Is a Learning Curve in Machine Learning?

>> Introduction to Convolutional Neural Networks

>> Introduction to the Classification Model Evaluation

>> Why Does the Cost Function of Logistic Regression Have a Logarithmic Expression?

>> Linear Regression vs. Logistic Regression

>> Bias in Neural Networks

>> How to Compute the Similarity Between Two Text Documents?

>> How to Build a Knowledge Graph?

>> Difference Between a Feature and a Label

>> Big Data vs Data Mining

>> Data Normalization Before or After Splitting a Data Set?

>> What Is the Difference Between Labeled and Unlabeled Data?

>> Introduction to Emotion Detection in Written Text

>> A Simple Explanation of Naive Bayes Classification

• Probability and Statistics

>> What Are the Prerequisites for Studying Machine Learning?

>> Advantages and Disadvantages of Neural Networks

>> Publicly Available Spam Filter Training Sets

>> Feature Scaling

>> PCA: Principal Component Analysis

>> Understanding Dimensions in CNNs

>> How ReLU and Dropout Layers Work in CNNs

>> Nonlinear Activation Functions in a Backpropagation Neural Network

>> Inadequacy of Linear Models: the Road to Nonlinear Functions

>> Genetic Algorithms vs Neural Networks

>> Predicates in Computer Science

>> Clustering Into an Unknown Number of Clusters

>> Introduction to Supervised, Semi-supervised, Unsupervised and Reinforcement Learning