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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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Transfer Learning

What is Transfer Learning?

Transfer learning is an approach used to transfer information from one machine learning task to another. Practically speaking, a pre-trained model that was trained for one task is re-purposed as the starting point for a new task. As a result, great amounts of time and resources can be saved by transfer learning.
Creating complex models from scratch requires vast amounts of compute resources, data, and time. Transfer learning accelerates the process by leveraging commonalities between tasks (such as detecting edges in images) and applying those learning to a new task. Training time for a model can go from weeks to hours, making machine learning more commercially viable for many businesses.
Source: Drivers of ML industrial success by Andrew Ng, 2016 NIPS
Transfer learning is very popular in domains like computer vision and NLP where large amounts of data are needed to produce accurate models.

An Example

Let's say we have merely 1,000 images of an object we want to classify. If we take a pre-trained CNN such as ResNet-50 which was trained on millions of images, we can re-train the model on our small dataset and build a state-of-the-art model with minimal effort. In neural networks, this is achieved by removing the final layer in the existing model (called the "loss output" layer) and replacing it with a new layer for the intended prediction.
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Tensor Processing Unit (TPU)
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Weights and Biases
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What is Transfer Learning?
An Example