and Cython. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level. This functionality brings a high level of flexibility and speed as a deep learning framework and provides accelerated NumPy-like functionality. PyTorch also includes standard defined neural network layers, deep learning optimizers, data loading utilities, and multi-gpu, and multi-node support performance and convergence from NVIDIA Volta™ tensor cores by using the latest deep learning example networks and model scripts for training. Each example model trains with mixed precision Tensor Cores on

"Population based training of neural networks." arXiv preprint arXiv:1711.09846 (2017). searched with the techniques that we discussed in this section. However, to truly design a Neural Network from scratch, different approach. The next section dives into the search for neural architectures. Neural Architecture Search On a high level, Neural Architecture Search (NAS) is similar to Hyperparameter Search. architecture. In fact, we could use HPO to decide whether adding a dropout layer is a good idea. Neural Architectures are composed of layers stacked on top of each other with a given layer processing the

Tseng） 2022.02.18 Outline ● Background: Prerequisites & What is Pytorch? ● Training & Testing Neural Networks in Pytorch ● Dataset & Dataloader ● Tensors ● torch.nn: Models, Loss Functions ● torch.optim: (like NumPy) on GPUs ○ Automatic differentiation for training deep neural networks Training Neural Networks Training Define Neural Network Loss Function Optimization Algorithm More info about the lecture video. Training & Testing Neural Networks Validation Testing Training Guide for training/validation/testing can be found here. Training & Testing Neural Networks - in Pytorch Validation Testing

is prepared for professionals who are aspiring to make a career in the field of deep learning and neural network framework. This tutorial is intended to make you comfortable in getting started with the ........................................................................... 11 Artificial Neural Networks ............................................................................................ ..... 12 Convolutional Neural Network (CNN) ........................................................................................................... 13 Recurrent Neural Network (RNN) ..........

Lecture 10: Neural Networks and Deep Learning Feng Li fli@sdu.edu.cn https://funglee.github.io School of Computer Science and Technology Shandong University Fall 2018 Deep Feedforward Networks • Also Also called feedforward neural networks or multilayer perceptrons (MLPs) • The goal is to approximate some function f ∗ • E.g., for a classifier, y = f ∗(x) maps an input x to a category y • A feedforward usually a highly non-linear function • Feedforward networks are of extreme importance to machine learning practioners • The conventional neural networks (CNN) used for object recognition from photos are

recall by repetition (i.e., increase the weight of that connection). Can we do the same with neural networks? Can we optimally prune the network connections, remove extraneous nodes, etc. while retaining compression ratio which results in lower transmission and storage costs. Figure 5-1 visually depicts two networks. The one on the left is the original network and the one on the right is its pruned version. Note compression ratio. Figure 5-1: An illustration of pruning weights (connections) and neurons (nodes) in a neural network consisting of fully connected layers. Exercise: Sparsity improves compression Let's import

Compression techniques are used to achieve an efficient representation of one or more layers in a neural network with a possible quality trade off. The efficiency goals could be the optimization of the these techniques can help reduce complexity and improve generalization. Let us consider an arbitrary neural network layer. We can abstract it using a function with an input and parameters such that . In the learnt for quantizing deep learning models. Looking Under the Hood As we know, one of the basic neural network operation is as follows: f(X; W, b) = σ(XW + b) Here, X, W and b are tensors (mathematical

Machine learning in turn is one approach towards artificial intelligence. Deep learning with neural networks has been the dominant methodology of training new machine learning models for the past decade Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105. do linear algebra operations training deep networks. However, one of the critical improvements in the past decade was the ReLU activation function. ReLU2 allowed the gradients to back-propagate deeper in the networks. Previous iterations

architectural breakthroughs in the field of neural networks. It introduced the idea of stacking layers to learn complex relationships. Convolutional Neural Nets (CNNs) were another important breakthrough breakthrough that enabled learning spatial features in the input. Recurrent Neural Nets (RNNs) facilitated learning from the sequences and temporal data. These breakthroughs contributed to bigger and bigger models sentence “the quick brown fox jumps over the lazy dog”, we can mask the word “jumps” and let the neural network predict the word it thinks fits in the sentence based on the surrounding words (context)

经网络结构，由谷歌团队于2019年提出，该 模型在图像分类、目标检测和图像分割等任 务中取得了不错的结果。 EfficientNet的设计思路来源于模型优化的 两个主要思想： 神经网络结构搜索（Neural Architecture Search，NAS）和模型融合。 其主要贡献在于开创性地提出了通过均匀缩 放（Accurate Scaling）来调整网络深度 、宽度和分辨率的方法。 23 3 Classification with Deep Convolutional Neural Networks (Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, 2012) 31 参考文献 • VGG：Very Deep Convolutional Networks for Large-Scale Image Recognition (Karen 2016) • DenseNet：Densely Connected Convolutional Networks (Gao Huang et al., 2017) • MobileNet ： MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (Andrew G. Howard