Tag: Neural Networks

>> Introduction to Large Language Models

>> What is and Why Use Temperature in Softmax?

>> How Do Self-Organizing Maps Work?

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

>> What’s a Non-trainable Parameter?

>> How Does a Neural Network Recognize Images?

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

>> Introduction to Landmark Detection

>> Image Recognition: One-Shot Learning

>> What Is Neural Style Transfer?

>> How Do Siamese Networks Work in Image Recognition?

>> Neural Networks: Pooling Layers

>> Co-occurrence Matrices and Their Uses in NLP

>> Deep Neural Networks: Padding

>> Single Shot Detectors (SSDs)

>> What Is Maxout in a Neural Network?

>> Recurrent Neural Networks

>> Graph Attention Networks

>> Sparse Coding Neural Networks

>> Differences Between Luong Attention and Bahdanau Attention

>> 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

>> VAE Vs. GAN For Image Generation

>> What Are Restricted Boltzmann Machines?

>> Introduction to Inception Networks

>> Cognitive Computing vs. Artificial Intelligence

>> Translation Invariance and Equivariance in Computer Vision

>> Neural Network and Deep Belief Network

>> Residual Networks

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

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

>> What Does Backbone Mean in Neural Networks?

>> Spatial Pyramid Pooling

>> Object Detection: SSD Vs. YOLO

>> Understanding Activation Functions

>> What Is Content-Based Image Retrieval?

>> What Are Channels in Convolutional Networks?

>> Instance Segmentation vs. Semantic Segmentation

>> How to Handle Large Images to Train CNNs?

>> Neurons in Neural Networks

>> Data Augmentation

>> What Is Fine-Tuning in Neural Networks?

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

>> Neural Networks: What Is Weight Decay Loss?

>> Neural Networks: Difference Between Conv and FC Layers

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

>> Bias Update in Neural Network Backpropagation

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

>> What Are “Bottlenecks” in Neural Networks?

>> Convolutional Neural Network vs. Regular Neural Network

>> Hidden Layers in a Neural Network

>> What Is Depth in a Convolutional Neural Network?

>> Activation Functions: Sigmoid vs Tanh

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

>> Calculate the Output Size of a Convolutional Layer

>> An Introduction to Generative Adversarial Networks

>> Linearly Separable Data in Neural Networks

>> Using GANs for Data Augmentation

>> How to Design Deep Convolutional Neural Networks?

>> How to Calculate Receptive Field Size in CNN

>> Open Source Neural Network Libraries

>> Encoder-Decoder Models for Natural Language Processing

>> Epoch in Neural Networks

>> Random Initialization of Weights in a Neural Network

>> Batch Normalization in Convolutional Neural Networks

>> Reinforcement Learning with Neural Network

>> Advantages and Disadvantages of Neural Networks Against SVMs

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

>> The Difference Between Epoch and Iteration in Neural Networks

>> SVM Vs Neural Network

>> Normalizing Inputs for an Artificial Neural Network

>> Introduction to Convolutional Neural Networks

>> Bias in Neural Networks

>> Advantages and Disadvantages of Neural Networks

>> How ReLU and Dropout Layers Work in CNNs

>> Nonlinear Activation Functions in a Backpropagation Neural Network

>> Genetic Algorithms vs Neural Networks