Deep Learning: Train Neural Networks and Deploy with Docker

Overview

This Deep Learning and Neural Networks in Production course equips you with the skills to design, train, and deploy neural networks using PyTorch, TensorFlow, FastAPI, and Docker. Whether you're building models from scratch or serving them in production, this course bridges the gap between deep learning theory and real-world deployment.

In Module 1, you'll explore the foundations of neural networks — building and training feed-forward networks, understanding activations, losses, and optimizers in PyTorch. Module 2 focuses on robust training and validation loops, experiment tracking with TensorBoard and Weights & Biases, and checkpoint analysis.

Module 3 covers packaging trained models for inference, serving them via FastAPI, and evaluating latency and reliability. Module 4 teaches containerization with Docker, production monitoring, logging, and scaling strategies.

By the end of this course, you will:

- Design and train neural networks using PyTorch and TensorFlow - Track and visualize model performance using TensorBoard and Weights & Biases - Serve trained deep learning models through FastAPI for real-time inference - Package, deploy, and scale deep learning applications with Docker in production Disclaimer:

This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated.

The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

Syllabus

• Foundations of Neural Networks
Covers the foundational concepts of neural networks including architecture, activations, losses, optimizers, and implementation in PyTorch.
• Model Training, Validation & Tracking
Focuses on implementing robust training and validation loops, tracking experiments using TensorBoard or Weights & Biases, and analyzing checkpoints for insights.
• Deploying Deep Learning Models
Covers packaging trained deep learning models for API inference, deploying models via FastAPI, and testing and measuring inference performance. Duration: 4 hours.
• Containerization & Production Integration
Covers containerizing deep learning APIs with Docker, integrating logging, error handling, and configuration, and deploying and scaling DL services in production. Duration: 4 hours.

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Taught by

Board Infinity

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Subjects

Computer Science