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Artificial Intelligence Wiki
Topics
Accuracy and Loss
Activation Function
AI Chips for Training and Inference
Artifacts
Artificial General Intelligence (AGI)
AUC (Area under the ROC Curve)
Automated Machine Learning (AutoML)
CI/CD for Machine Learning
Comparison of ML Frameworks
Confusion Matrix
Containers
Convergence
Convolutional Neural Network (CNN)
Datasets and Machine Learning
Data Science vs Machine Learning vs Deep Learning
Distributed Training (TensorFlow, MPI, & Horovod)
Generative Adversarial Network (GAN)
Epochs, Batch Size, & Iterations
ETL
Features, Feature Engineering, & Feature Stores
Gradient Boosting
Gradient Descent
Hyperparameter Optimization
Interpretability
Jupyter Notebooks
Kubernetes
Linear Regression
Logistic Regression
Long Short-Term Memory (LSTM)
Machine Learning Operations (MLOps)
Managing Machine Learning Models
ML Showcase
Metrics in Machine Learning
Machine Learning Models Explained
Model Deployment (Inference)
Model Drift & Decay
Model Training
MNIST
Overfitting vs Underfitting
Random Forest
Recurrent Neural Network (RNN)
Reproducibility in Machine Learning
REST and gRPC
Serverless ML: FaaS and Lambda
Synthetic Data
Structured vs Unstructured Data
Supervised, Unsupervised, & Reinforcement Learning
TensorBoard
Tensor Processing Unit (TPU)
Transfer Learning
Weights and Biases
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Convolutional Neural Network (CNN)
Source: Towards Data Science
A Convolutional Neural Network (CNN), sometimes referred to as a ConvNet, is the most well-known image recognition and classification algorithm. CNNs were one of the key innovations that led to the deep neural network renaissance in computer vision, which is a subset of machine learning.
A typical CNN consists of a combination of convolutional, pooling, and dense layers.

Run a CNN sample project from the ML Showcase!

https://ml-showcase.paperspace.com/projects/classifying-clothing-images-with-fashion-mnist
ml-showcase.paperspace.com
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