Artificial Intelligence

>> 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?

>> 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

>> 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

>> 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’s a Hypothesis Space?

>> 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?

>> 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 Adoption

>> 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?

>> 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?

>> 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

>> 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?

>> Stratified Sampling in Machine Learning

>> Outlier Detection and Handling

>> 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

>> Pattern Recognition in Time Series

>> How to Create a Smart Chatbot?

>> Open-Source AI Engines

>> NLP’s word2vec: Negative Sampling Explained

>> LL vs. LR Parsing

>> How to Calculate Receptive Field Size in CNN

>> k-Nearest Neighbors and High Dimensional Data

>> State Machines: Components, Representations, Applications

>> 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

>> Semantic Similarity of Two Phrases

>> Why Feature Scaling in SVM?

>> Automatic Keyword and Keyphrase Extraction

>> Generalized Suffix Trees

>> Normalization vs Standardization in Linear Regression

>> Word Embeddings: CBOW vs Skip-Gram

>> String Similarity Metrics: Sequence Based

>> How to Improve Naive Bayes Classification Performance?

>> 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

>> 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

>> 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

>> 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