both CPU and GPU.  Highly scalability of computation. Benefits Keras is highly powerful and dynamic framework and comes up with the following advantages:  Larger community support.  Easy list of initializers function. We can learn it in details in Keras layer chapter. during model creation phase of machine learning.  Regularizers: Provides a list of regularizers function. We can

extension to GPUs. • Computational graphs − PyTorch provides an excellent platform which offers dynamic computational graphs. Thus a user can change them during runtime. • It includes many layers as Torch research.google.com/ Misc • Dynamic VS Static Computation Graph a b x_train_tensor Epoch 1 Misc • Dynamic VS Static Computation Graph a b yhat x_train_tensor Misc • Dynamic VS Static Computation Graph y_train_tensor Misc • Dynamic VS Static Computation Graph a b x_train_tensor Epoch 2 Misc • Dynamic VS Static Computation Graph a b yhat x_train_tensor Misc • Dynamic VS Static Computation Graph

2014 • word2vec • XLNet • RoBERTa • GPT-2 • T5 • GloV e Static Representation Dynamic Representation Deep Dynamic Representation 深度学习入门-NLP 21 深度学习入门-NLP 2022chatGPT 22 2. 神经网络的基础 01

final output of the model is the word itself. Let’s discuss each step in detail. Step 1: Vocabulary Creation In this step, we create a vocabulary of the top words10 (ordered by frequency) from the given scratch. Let’s review those four steps, and see how they apply in our case here. Step 1: Vocabulary Creation In this step, we will use a TextVectorization layer from Tensorflow to create a vocabulary of the

the earlier section. Active Research Some recent works like Sparse Evolutionary Training5 (SET), Dynamic Sparse Reparametrization6 (DSR) and Sparse Networks from Scratch7 (SNFS) have introduced an additional Mostafa, Hesham, and Xin Wang. "Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization." International Conference on Machine Learning. PMLR, 2019. 5 Mocanu,

networks Memory-Augmented Neural Networks Source: Hybrid computing using a neural network with dynamic external memory Work Fun Play Hard

解空间 未来⽅向—现有推荐架构的问题，算法⼯程协同的解法 � 更基础的复杂模型，场景的快速适应 � 多场景建模 � 端云⼀体的协同 推荐技术 [KDD2020] DCAF: A Dynamic Computation Allocation Framework for Online Serving System � 推荐全链路⾃适应 � 统⼀建模，根据请求量削峰填⾕，资源利⽤最⼤化

模型并行(Whale)  FP16 / Int8  模型剪枝  Op融合(Fusion Stitch)  MILR: Blade Disc 工程优化: Blade模型推理 Dynamic Shape Compiler for Machine Learning Workloads EmbeddingVariable [No Hash Conflict] 特征准入/淘汰 Adaptive

et al, 2013], etc. Previous Work 77 Data from [Whyte et al, 2010] Different Blur Assumptions Dynamic: [Kim et al, 2013], [Kim et al, 2014], [Nah et al, 2017], etc. Previous Work 78 Data from [Kim

performance regression of up to 17% for workloads using dynamic input shapes. ‣ Tacotron2 inference performance regression of up to 15% for workloads using dynamic input shapes. Security CVEs ‣ CVE-2022-45198