Generative AI from First Principles-Article 4: Loss Functions, Optimizers, Layers, and Training…

📰 Medium · Deep Learning

Learn the fundamentals of generative AI, including loss functions, optimizers, layers, and training mechanics, to build a strong foundation in deep learning

intermediate Published 29 Apr 2026

Action Steps

Apply different loss functions to a neural network using TensorFlow or PyTorch to see how they affect model performance
Configure and compare various optimizers, such as Adam and SGD, to optimize model training
Build and train a simple generative model using a library like Keras or TensorFlow
Experiment with different layer architectures, such as convolutional or recurrent layers, to improve model performance
Train a model using a dataset like MNIST or CIFAR-10 to practice implementing training mechanics

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this article to improve their understanding of generative AI and apply it to real-world problems

Key Insight

💡 Understanding the core components of generative AI, including loss functions, optimizers, and layer architectures, is crucial for building effective models

Key Takeaways

Learn the fundamentals of generative AI, including loss functions, optimizers, layers, and training mechanics, to build a strong foundation in deep learning

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Title: Generative AI from First Principles-Article 4: Loss Functions, Optimizers, Layers, and Training…

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Published Time: 2026-04-29T05:51:03Z

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# Generative AI from First Principles-Article 4: Loss Functions, Optimizers, Layers, and Training Mechanics

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In the previous article, we understood the core learning loop of a neural network: a model makes a prediction in the forward pass, we measure how

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