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An in-depth look at Adam and AdamW optimization algorithms for training large language models. Explores the key differences and advantages of AdamW for improved generalization.
Discover how the Adam optimization algorithm combines momentum and RMSprop to accelerate neural network training. Learn about its implementation, hyperparameters, and widespread adoption in deep learning.
Learn how to fine-tune the latest open-source language models like Gemma, Qwen, Llama, and Mistral using Unsloth and Transformers libraries. This guide covers data preparation, hyperparameter tuning, and evaluation techniques.
An in-depth look at the process of creating large language models, covering pre-training, post-training, data collection, evaluation, and system optimizations.
Learn how to install PyTorch on your local system, including setup for Apple Silicon devices. This guide covers creating virtual environments, installing essential data science libraries, and verifying PyTorch installation.
Mode collapse is a common issue in GAN training where the generator produces limited variety. This article explores the causes, effects and solutions for mode collapse in generative adversarial networks.
An in-depth exploration of convolutional neural networks (CNNs), covering their structure, operations, and implementation in PyTorch. Learn about convolution layers, pooling, and the feature extraction process.
An in-depth exploration of back propagation in neural networks, covering forward and backward passes, loss functions, gradient descent, and weight updates.
An in-depth look at the evolution of sequence modeling techniques in machine learning, from early autoregressive models to modern Transformer architectures.