Deep Learning (Part-03): Basics of the Neural Network Training Process

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Learn the basics of the neural network training process in deep learning, including data preparation, model initialization, and optimization techniques.

intermediate Published 20 Jun 2026

Action Steps

Prepare a dataset for training by preprocessing and splitting it into training and testing sets.
Initialize a neural network model with a suitable architecture and hyperparameters.
Compile the model with a loss function and optimizer.
Train the model using the training dataset and evaluate its performance on the testing dataset.
Fine-tune the model by adjusting hyperparameters and optimizing techniques.

Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding the neural network training process to improve model performance and accuracy.

Key Insight

💡 Understanding the neural network training process is crucial for building accurate and efficient deep learning models.

Key Takeaways

Learn the basics of the neural network training process in deep learning, including data preparation, model initialization, and optimization techniques.

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Title: Deep Learning (Part-03): Basics of the Neural Network Training Process

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Published Time: 2026-06-20T21:49:01Z

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# Deep Learning (Part-03): Basics of the Neural Network Training Process | by 0s & 1s — All About Software ✨ | Jun, 2026 | Medium

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# Deep Learning (Part-03): Basics of the Neural Network Training Process

## The General Process of Training a DL Model — Regardless of the Network Architecture

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Deep Learning (Part-03): Basics of the Neural Network Training Process

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