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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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Gradient Descent
Source: O'Reilly Media
Gradient descent is an iterative optimization algorithm used in machine learning to minimize a loss function.
The loss function describes how well the model will perform given the current set of parameters (weights and biases) and gradient descent is used to find the best set of parameters. This is achieved by taking the partial derivative at a given point and then iteratively traversing the search space in the negative direction of the function gradient.
As the loss function improves, the parameters of a model (weights) are updated until it reaches the optimal point which is the minima of the loss function (the weights are updated in proportion to the derivative of the error). The two key aspects of Gradient descent are a) the direction to move and b) the size of the step (learning rate, discussed below).
Gradient Descent in action
Gradient descent is used when the model parameters cannot be calculated using straightforward math (e.g., linear algebra) and must be searched for using an optimization algorithm.
There are several variants of gradient descent including batch, stochastic, and mini-batch.
There are also several optimization algorithms including momentum, adagrad, nesterov accelerated gradient, RMSprop, adam, etc. Here is a blog post that covers the differences between these algorithms.
Gradient descent has a parameter called learning rate which represents the size of the steps taken as that network navigates the curve in search of the valley. If the learning rate is too high, the network may overshoot the minimum. If it's too low, the training will take too long and may never reach the minimum, or else get stuck in local minima.
Source: Rohith Gandhi / Towards Data Science
Check out the in-depth explanation of Gradient Descent in this blog post.
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Gradient Boosting
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Hyperparameter Optimization
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