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. script is available on GitHub and NGC. ‣ Mask R-CNN model: Mask R-CNN is a convolution-based neural network that is used for object instance segmentation. PyTorch Release 23.07 PyTorch RN-08516-001_v23.07

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 the model’s performance? In this chapter depicts two networks. The one on the left is the original network and the one on the right is its pruned version. Note that the pruned network has fewer nodes and some retained nodes have fewer connections 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 the

training process: performance and convergence. Hyperparameters like number of filters in a convolution network or 1 Note that this search space is just choosing if we are applying the techniques. The techniques extended beyond training parameters to structural parameters that can manipulate the structure of a network. The number of dense units, number of convolution channels or the size of convolution kernels can searched with the techniques that we discussed in this section. However, to truly design a Neural Network from scratch, we need a different approach. The next section dives into the search for neural architectures

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 Keras framework 12 Convolutional Neural Network (CNN) ........................................................................................................... 13 Recurrent Neural Network (RNN) .................. ........................................................... 71 12. Keras ― Convolution Neural Network ................................................................................................

模块的输出??连同它的网络层参数??和??等称为一层网络层。特别地，对于网络中间的 层，也叫作隐藏层，最后一层也叫作输出层。这种由大量神经元模型连接形成的网络结构 称为神经网络(Neural Network)。从这里可以看到，神经网络并不难理解，神经网络每层的 节点数和神经网络的层数或结构等决定了神经网络的复杂度。 预览版202112 第 3 章 分类问题 10 输入层：? 处理高维度的图片、视频数据时往往出现网络参数量巨大，训练非常困难的问题。通过利 用局部相关性和权值共享的思想，Yann Lecun 在 1986 年提出了卷积神经网络 (Convolutional Neural Network，简称 CNN)。随着深度学习的兴盛，卷积神经网络在计算机 视觉中的表现大大地超越了其它算法模型，呈现统治计算机视觉领域之势。这其中比较流 行的模型有用于图片分类的 AlexNet、VGG、Go 处理并理解文本数据是自然语言处理 的一个核心问题。卷积神经网络由于缺乏 Memory 机制和处理不定长序列信号的能力，并 不擅长序列信号的任务。循环神经网络(Recurrent Neural Network，简称 RNN)在 Yoshua Bengio、Jürgen Schmidhuber 等人的持续研究下，被证明非常擅长处理序列信号。1997 预览版202112 6.8 汽车油耗预测实战

differentiation for training deep neural networks Training Neural Networks Training Define Neural Network Loss Function Optimization Algorithm More info about the training process in last year's lecture Networks – in Pytorch Define Neural Network Loss Function Optimization Algorithm Training Validation Testing Step 2. torch.nn.Module Load Data torch.nn – Network Layers ● Linear Layer (Fully-connected (10, 5, 32), (1, 1, 3, 32), ... torch.nn – Network Layers ● Linear Layer (Fully-connected Layer) ref: last year's lecture video torch.nn – Neural Network Layers ● Linear Layer (Fully-connected Layer)

to achieve the desired model quality on our task. 2. Fine-tuning: This step adapts a pre-trained network to a specific task. Fine-tuning is compute efficient since we reuse the same base model for all the They demonstrate that using a network pre-trained in this fashion improves the quality of the final object detection task, as compared to randomly initializing the network. Similarly, another task is to 6-4 (b)). The authors report that the network trained in a self-supervised manner this way can be fine-tuned to perform nearly as well as a fully supervised network. 3 Gidaris, Spyros, et al. "Unsupervised

models, the performance of the model scaled well with the number of labeled examples, since the network had a large number of parameters. Thus to extract the most out of the setup, the model needed a large in neural networks has led to an increase in the network complexity, number of parameters, the amount of training resources required to train the network, prediction latency, etc. Natural language models point values to 8-bit unsigned / signed integers). Quantization can generally be applied to any network which has a weight matrix. It can often help reduce the model size 2 - 8x, while also speeding up

fed to improve the target models. 12 Hendrycks, Dan, and Thomas Dietterich. "Benchmarking neural network robustness to common corruptions and perturbations." arXiv preprint arXiv:1903.12261 (2019). 11 introduce techniques like Synthetic Minority Oversampling Technique16 (SMOTE) and Generative Adversarial Network17 (GAN) which can generate synthetic data for images. While SMOTE leverages statistical models for learning for this purpose. A GAN is composed of two neural networks: a generator network and a discriminator network as shown in figure 3-15. The generator creates synthetic samples from random inputs

function f ∗ • E.g., for a classifier, y = f ∗(x) maps an input x to a category y • A feedforward network defines a mapping y = f(x; θ) and learns the value of the parameters θ that result in the best function neural networks (CNN) used for object recognition from photos are a specialized kind of feedforward network • It can be extended to recurrent neural networks (RNN) by involving feedback connections, which thing we must provide to the neural network is a suffcient number of training examples (x(i), y(i)) • It can be diffcult to understand the features a neural network has invented; therefore, people refer