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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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  1. Topics

Model Training

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Last updated 5 years ago

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Training is where a machine learning system finds the ideal parameters of a model on its own. In the most common scenario ( with labeled training data), the model learns these parameters directly from the training data.

While training data is used to find optimal parameters of the model, the object of machine learning is to find the best set of parameters that produces accurate outcomes on new, unseen data, not training data.

The ML community has converged on terms like “jobs” and “experiments” to describe the iterative model-training process where each job or experiment represents a new iteration. This is similar to a code commit in software development. Generally speaking, this process involves executing code written locally in an IDE or remotely on a GPU or CPU instance (or cluster of instances in the case of distributed training).

Training + Gradient

Choose from a wide variety of templates that include all the frameworks, libraries, and drivers you need for machine learning. Customer dependencies can be installed in any notebook and dependencies are persistent across sessions.

Gradient from provides an interface to track model training and deployment.

structure your machine learning projects with automatic versioning, tagging, and life-cycle management. Experiments include hyperparameter search, distributed training, a Git integration, and infrastructure automation (job scheduling, unified logs, cluster management, and more).

Gradient also includes a Jupyter Notebook integration where a GPU-enabled Jupyter Notebook can be launched from your browser in seconds. are fully-managed and do not require any setup or management of servers or dependencies.

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Experiments
Gradient Notebooks
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