DL ML CNN 相关的经验之谈

最新推荐文章于 2024-05-28 10:28:09 发布

原创最新推荐文章于 2024-05-28 10:28:09 发布 · 612 阅读

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本文深入探讨了深度学习领域的核心模型和技术，包括卷积神经网络（CNN）、全连接神经网络、支持向量机（SVM）、循环神经网络（如LSTM和GRU）、生成对抗网络（GAN）及自编码器等。同时，介绍了这些技术在图像处理、自然语言处理等领域的应用，并提供了编程语言和库的选择建议。

1，CNN：目前最合适的卷积层数：20～25层

numpy pandas matplotlib scipy sympy pylab

scikit-learn

模型准确性

notes

tensorflow scipy

face net

MTCNN算法

Maths:
1. Linear algebra
2. Probability and statistics
3. Numerical optimization
4. Calculus
Image processing: or generally digital signal processing.
1. Digital filters: Gaussian blur
2. Image transformations: rotations, translations, scaling and many more.
3. Scale space theorem for natural stimuli processing. Basically implemented using image pyramids, the so called multi-scale approach.
Machine learning (ML): A very important area that is changing many AI sub-fields. You need to know about:
1. Convolutional neural networks (CNN).
2. Fully connected neural networks.
3. Support vector machines (SVM)
4. Recurrent neural networks (RNN) such as long-short-term-memory (LSTM) or gated recurrent unit (GRU) networks.
5. Generative adversarial networks (GAN): For many tasks like image-to-image translation. Image enhancements, art style transfer and so on.
6. Autoencoders: For noise reduction and image compression.
Programming:
1. C/C++: You can use OpenCV for CV implementations or build your own from scratch using C/C++.
2. Java: OpenCV is also available in Java.
3. Python: You can again use OpenCV in Python language. Probably this is a better choice as Python is a very high-level easy to learn language.