Click on the above “Beginner Learning Vision”, choose to add Star or Top ” Important content delivered at the first time Author | Rohan Jagtap Compiled by | ronghuaiyang Source | AI Park TensorFlow 2.x provides a lot of simplicity in building models and the overall use of TensorFlow. In this article, we will explore … Read more
Using data augmentation techniques can increase the diversity of images in the dataset, thereby improving the performance and generalization ability of the model. The main image augmentation techniques include: Resizing Grayscale Transformation Normalization Random Rotation Center Cropping Random Cropping Gaussian Blur Brightness and Contrast Adjustment Horizontal Flip Vertical Flip Gaussian Noise Random Blocks Central Region … Read more
Follow the WeChat public account “ML_NLP“ Set as “Starred“, to receive heavy content promptly! Reprinted from: AI Park Author: Sam Nolen Translation: ronghuaiyang Introduction Applicable in cases with very few samples. Even imperfect synthetic data can improve classifier performance. Generative Adversarial Networks (GANs) were introduced by Ian Goodfellow in 2014 and have become a very … Read more
The accurate prediction of contaminants in the food processing process is of great significance for food safety. However, due to the complexity of food processing technology and the difficulty in detecting contaminants, the amount of data is relatively small, making it difficult to meet the requirements for modeling and prediction. Therefore, it is necessary to … Read more
Click on the above “Beginner Learning Vision“, select to add “Star” or “Top“ Essential insights delivered promptly Reprinted from: Author | Charlotte Source | Deep Learning Enthusiasts Editor | Jishi Platform Summary of tuning techniques, all about CNN optimization. Summary of CNN Optimization Systematic evaluation of CNN advances on the ImageNet Using ELU non-linearity without … Read more
Click on "XiaoBai Learns Vision" above, choose to add "Star" or "Top" Heavy content delivered at the first time Introduction This article aims to address overfitting in neural networks. Overfitting will be your main concern as you train the model with only 2000 data samples.There are some methods to help overcome overfitting, namely dropout and … Read more
Click the “CVer” above and select “Star” to pin it Heavyweight content delivered first If we were to rank several stages in the deep learning development process by importance, preparing training data would surely be among the top few. It’s important to understand that once a model network is written, it is merely a chunk … Read more
1 Compiled by New Intelligence Source: blog.kaggle.com Compiled by: Jia Yuepeng [New Intelligence Guide]The champion team of the Kaggle Ocean Fish Recognition and Classification Competition shares their technology: How to design robust optimization algorithms? How to analyze data and perform data augmentation? Technical details include using images from different boats for validation and how to … Read more
The powerful feature representation and nonlinear fitting capability of deep neural networks stem from sufficient learning on high-quality datasets. However, in practical engineering applications, due to economic and labor costs, acquiring a large amount of typical labeled data becomes extremely challenging, resulting in avery limited number of training samples. Data augmentation methods provide a simple … Read more
Machine Heart Column Author: Zhang Junlin BERT has brought great surprises to people, but in the blink of an eye, about half a year has passed, and during this time, many new works related to BERT have emerged. In recent months, aside from my main work on recommendation algorithms, I have been quite curious about … Read more