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Pre-Trained Models

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May 1, 2024 Updated June 22, 2025 20 minute read

Navigating the World of Pre-Trained Models

Pre-trained models represent a significant leap forward in the field of artificial intelligence and machine learning. At a high level, a pre-trained model is an AI model that has already been trained on a vast amount of data for a general task, such as understanding language or recognizing images. This initial training captures a wealth of knowledge and patterns, which can then be adapted for more specific tasks with significantly less data and computational resources than training a model from scratch. This approach not only accelerates development but also often leads to better performance, especially when specific data is scarce.

Path to Pre-Trained Models

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We've curated eight courses to help you on your path to Pre-Trained Models. Use these to develop your skills, build background knowledge, and put what you learn to practice.

Sorted from most relevant to least relevant:

Applying Neural Networks: A Guide to Pre-trained Models

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Zero-Shot & Few-Shot Learning: Master AI with Minimal Data

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Image Classification with PyTorch

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Generative AI: Foundation Models and Platforms

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【基礎から学ぶ】自然言語処理から大規模言語モデルLLMへ~Pythonで実践してみよう

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Applications of Computer Vision & Deep Learning

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NLP实战-Huggingface神器

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Applying Machine Learning to Your Data with GC - Français

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Reading list

We've selected four books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Pre-Trained Models.

Hands-On Transfer Learning with Python

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Provides a comprehensive guide to transfer learning, covering the fundamentals, techniques, and applications. It is particularly relevant for practitioners who want to leverage pre-trained models to improve the performance of their own models.

Hands-On Transfer Learning with Python: Implement...

Kindle Edition

Deep Learning

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Provides a comprehensive overview of deep learning, including a chapter on transfer learning. It foundational resource for practitioners who want to understand the fundamentals of deep learning and how to apply transfer learning techniques.

Advanced Deep Learning with Keras

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Covers advanced deep learning techniques, including a section on transfer learning. It is suitable for practitioners who have a good understanding of the basics of deep learning and want to explore more advanced topics.

Advanced Deep Learning with Keras

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Advanced Deep Learning with Keras

Kindle Edition

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Grokking Deep Learning

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Provides a high-level overview of machine learning, including a chapter on transfer learning. It good starting point for practitioners who want to get a broad understanding of machine learning and its applications.

Grokking Deep Learning

Kindle Edition

Pre-Trained Models

Navigating the World of Pre-Trained Models

Path to Pre-Trained Models

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Reading list