9 posts
Basic implementation methods for object detection, starting with object localization and summarizing object detection algorithms such as landmark detection, object detection, the sliding-window method, and NMS (Non-Max Suppression).
This article introduces several classic neural network models, including the network architectures of LeNet-5, AlexNet, VGG-16, ResNet, Inception networks, and more.
Why do neural networks often use convolution when analyzing images? Why are there padding and stride? What roles do pooling layers and fully connected layers play?
This article introduces common parameter-selection principles and approximate ranges in neural networks, as well as the basic principles of BN and softmax.
This article introduces several of the most commonly used optimization algorithms, starting from partitioning the dataset into mini-batches, then covering momentum gradient descent, RMSprop, and their combination Adam, along with implementation details and basic principles for each method.
There are many ways to improve neural network training performance, such as avoiding underfitting and overfitting, using regularization, and so on. Different methods also have different effects.
Linear regression in deep learning, basic optimization algorithms, and implementations of Softmax regression and loss functions.
Introduces how to perform basic data operations with PyTorch and pandas, and how to carry out fundamental linear algebra operations and derivative computation with torch.
Notes from my journey learning deep learning from scratch. This chapter introduces the most fundamental mathematical ideas behind deep learning, as well as simple regression-based approaches to deep learning algorithms and related code.
