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Do You Need to Read Elements of Statistical Learning Before Bengios Deep Learning Textbook?
Do You Need to Read Elements of Statistical Learning Before Bengio's Deep Learning Textbook?
When venturing into the field of deep learning, the choice of textbooks can significantly impact your learning journey. One common question that arises is whether one should read Elements of Statistical Learning (EoSL) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman before moving on to Yoshua Bengio's comprehensive Deep Learning.
Self-Contained Nature of Bengio's Deep Learning Textbook
Bengio has intentionally written Deep Learning to be self-contained, meaning it includes the necessary background and contextual information to understand and engage with the material. Each chapter is designed to be accessible and comprehensive, providing a balanced mix of theoretical foundations and practical applications. By devoting significant effort to ensure the book is self-sufficient, Bengio aims to make deep learning learning accessible to a wide range of readers, from beginners to advanced learners.
Importance of Prior Learning in Statistical Learning
That being said, having prior knowledge of statistical learning concepts can indeed be beneficial. Statistical learning forms the bedrock of many deep learning techniques. Many of the fundamental concepts, such as regression, classification, and dimensionality reduction, are closely related to deep learning principles. Understanding these foundational concepts can help connect the dots and enhance your comprehension of the more advanced topics in deep learning. For instance, concepts like model validation, regularization, and feature selection from EoSL are deeply intertwined with deep learning techniques like dropout, batch normalization, and data preprocessing.
Practical Recommendation for Learning Deep Learning
If you have a solid background in statistical learning, you might find it easier to quickly grasp specific aspects of deep learning. On the other hand, if starting from scratch, beginning with Bengio's book can provide a more holistic and structured introduction to the subject. However, there is no hard and fast rule. Here are some key considerations:
Understanding Background Concepts: If you are familiar with topics such as linear regression, logistic regression, or decision trees, these foundational concepts will make it easier to understand neural networks and their variants. Comprehensive Coverage: If you prefer a comprehensive introduction that delves into advanced topics like differential geometry and optimization, Bengio's book might be the way to go. Learning Pace: If you learn best with a structured and self-contained curriculum, start with Bengio's book. If you want to build a bridge between statistical and machine learning, reading EoSL first could be beneficial.Conclusion
In summary, while it is not strictly necessary to read Elements of Statistical Learning before Bengio's Deep Learning, doing so can provide a more robust foundation for your learning journey. The key is to find the learning path that best suits your current knowledge, learning style, and long-term goals. Whether you choose to start with Bengio's book or supplement it with EoSL, the most important thing is to get started and build a strong understanding of deep learning fundamentals.