332. TorchServe
Deploying Your Model TorchServe allows you to expose a WEB API for your Pytorch model that may be accessed directly or via your application. 3 Steps Choose a default handler or author a custom model handler. You will define a…
Category AI
331. Basics Of Training Large Models
Two Frameworks There are mainly two large frameworks for training large-scale deep learning models. Data Parallelism This method is usually taken whenever your model CAN fit completely into your GPU memory, sending different batches of data for each GPU. Model…
229. Quantization using Pytorch
Quantization Quantization is a technique to change the data type used to compute neural networks for faster inference. After you’ve deployed your model, there is no need to backpropagate(which is sensitive to precision). This means, if a slight decrease in…
228. Pruning Using Pytorch
Pruning State-of-the-art deep learning techniques rely on over-parameterized models which makes it hard when the deploying destination has limited resources. Pruning is used to learn the differences between over-parameterized and under-parameterized networks and sparsify your neural networks. In Pytorch, you…
227. Polynomial Features
Adding Linear Complexity When we want to train a model, we can easily imagine that we are unable to capture patterns of the training data if only using straight lines. Polynomial features is useful when you want to add more…
226. Training Methods for EBMs
Constrastive Method Push down on the energy of training samples while pulling up on the energies of suitably placed contrastive samples. The Disadvantage is that you always need contrastive samples in order to constrain the low-energy region. Regularized Method Push…
225. Latent Variable Energy-Based Model
World Model If you haven’t read my previous blog post about the “world model” please go check it out. Training the world model is a prototypical example of Self-Supervised Learning; Learning the mutual dependencies between its inputs. It is said…
224. Model Architecture For Autonomous Intelligence
Architecture Today I’d like to share a system architecture for autonomous intelligence proposed by Yann LeCun. The architecture consists of the following modules. Perception Perceive the world through multiple sensors and predict the current state Actor Predicts a sequence of…
223. Contrastive Learning
Contrastive Learning Contrastive learning is a technique used to optimize computer-vision-related tasks by comparing multiple samples against each other to learn the attribute to identify the difference between each class.
222. Siamese Architecture Comparison
Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. Here is a visual comparison of 4 different Siamese architectures.