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LinkedIn Learning

Full-Stack Deep Learning with Python (2024)

via LinkedIn Learning

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Overview

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Increase your knowledge and get a hands-on understanding of full-stack deep learning with Python.

Syllabus

Introduction
  • Full-stack deep learning, MLOps, and MLflow
  • Prerequisites
1. An Overview of Full-Stack Deep Learning
  • Introducing full-stack deep learning
  • Introducing MLOps
  • Introducing MLflow
  • Setting up the environment on Google Colab
  • Running MLflow and using ngrok to access the MLflow UI
2. Model Training and Evaluation Using MLflow
  • Loading and exploring the EMNIST dataset
  • Logging metrics, parameters, and artifacts in MLflow
  • Set up the dataset and data loader
  • Configuring the image classification DNN model
  • Training a model within an MLflow run
  • Exploring parameters and metrics in MLflow
  • Making predictions using MLflow artifacts
3. Model Training and Hyperparameter Tuning
  • Preparing data for image classification using CNN
  • Configuring and training the model using MLflow runs
  • Visualizing charts, metrics, and parameters on MLflow
  • Setting up the objective function for hyperparameter tuning
  • Hyperparameter optimization with Hyperopt and MLflow
  • Identifying the best model
  • Registering a model with the MLflow registry
4. Model Deployment and Predictions
  • Setting up MLflow on the local machine
  • Workaround to get model artifacts on the local machine
  • Deploying and serving the model locally
Conclusion
  • Summary and next steps

Taught by

Janani Ravi

Reviews

4.6 rating at LinkedIn Learning based on 83 ratings

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