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神經架構搜索(NAS)是一個很有前途的領域。首先，我將討論圍繞NAS建立科學社區的各種工作，包括基準測試、最佳實踐和開放源碼框架。然后，我將討論該領域幾個令人興奮的方向:(1)廣泛的NAS加速技術;(2)在Auto-PyTorch中結合NAS +超參數優化，實現現成的AutoML;(3)神經集成搜索(NES)的擴展問題定義，它搜索一組互補的架構，而不是像NAS中搜索的單一架構。

香港中文大學Bei Yu開設的深度學習課程，重點對神經網絡加速高效計算進行講解。

雖然大多數流行和成功的模型架構都是由人工專家設計的，但這并不意味著我們已經探索了整個網絡架構空間并確定了最佳選擇。如果我們采用一種系統的、自動的方式來學習高性能模型體系結構，那么我們將更有可能找到最佳的解決方案。

自動學習和演化網絡拓撲并不是一個新想法(Stanley & Miikkulainen, 2002)。近年來，Zoph & Le 2017和Baker等人在2017年的開創性工作吸引了神經架構搜索(NAS)領域的大量關注，為更好、更快、更經濟的NAS方法帶來了許多有趣的想法。

當我開始研究NAS時，我發現Elsken等人2019年的這項調查非常有幫助。它們將NAS描述為一個由三個主要成分組成的系統，簡潔明了，也被其他NAS論文廣泛采用。

• 搜索空間: NAS搜索空間定義了一組操作(例如卷積、全連接、池化)以及如何將操作連接起來形成有效的網絡架構。搜索空間的設計通常涉及人類的專業知識，以及不可避免的人類偏見。

• 搜索算法: NAS搜索算法對網絡體系結構候選對象進行采樣。它接受子模型性能指標作為獎勵(例如，高精度，低延遲)，并優化生成高性能架構候選。

• 評估策略: 我們需要測量、估計或預測提出的大量子模型的性能，以獲得反饋，供搜索算法學習。候選評估的過程可能非常昂貴，許多新的方法被提出來節省時間或計算資源。

一份來自FarizDarari的簡明教程

當前關于機器學習方面的資料非常豐富：Andrew NG在Coursera上的機器學習教程、Bishop的《機器學習與模式識別》 和周志華老師的《機器學習》都是非常好的基礎教材；Goodfellow等人的《深度學習》是學習深度學習技術的首選資料；MIT、斯坦福等名校的公開課也非常有價值；一些主要會議的Tutorial、keynote也都可以在網上搜索到。然而，在對學生們進行培訓的過程中， 我深感這些資料專業性很強，但入門不易。一方面可能是由于語言障礙，另一個主要原因在于機器學習覆蓋 面廣，研究方向眾多，各種新方法層出不窮，初學者往往在各種復雜的名詞，無窮無盡的 算法面前產生畏難情緒，導致半途而廢。

本書的主體內容是基于該研討班形成的總結性資料。基于作者的研究背景，這本書很難說 是機器學習領域的專業著作，而是一本學習筆記，是從一個機器學習 技術使用者角度對機器學習知識的一次總結，并加入我們在本領域研究中的一些經驗和發現。與其說是一本教材，不如說是一本科普讀物， 用輕松活潑的語言和深入淺出的描述為初學者打開機器學習這扇充滿魔力的大門。打開大門以后，我們會發現這是個多么讓人激動人心的 領域，每天都有新的知識、新的思路、新的方法產生，每天都有令人振奮的成果。我們希望這本書 可以讓更多學生、工程師和相關領域的研究者對機器學習產生興趣，在這片異彩紛呈的海域上找到 屬于自己的那顆貝殼。

強烈推薦給所有初學機器學習的人，里面有： 書籍的pdf 課堂視頻 課堂slides 各種延伸閱讀 MIT等世界名校的slides 學生的學習筆記等

使用Python進行計算機視覺的深度學習將使您成為計算機視覺和視覺識別任務的深度學習專家。

在書中，我們將重點學習:

• 神經網絡和機器學習
• 卷積神經網絡(CNNs)
• 目標檢測/定位與深度學習
• 訓練大型(圖像級)網絡
• 掌握使用Python編程語言和Keras、TensorFlow 2.0和mxnet深度學習庫的實現

在用Python進行了計算機視覺的深度學習之后，您將能夠用深度學習解決實際問題。

下載地址：鏈接: //pan.baidu.com/s/1I8r-Vjvv4n8v-6t_5I679g 提取碼: j69b

Real-time Federated Evolutionary Neural Architecture Search (Zhu and Jin. 2020) //arxiv.org/abs/2003.02793

BATS: Binary ArchitecTure Search (Bulat et al. 2020)

ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search (Zhang et al. 2020)

NAS-Count: Counting-by-Density with Neural Architecture Search (Hu et al. 2020)

ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures (Kefan and Pang. 2020)

Neural Inheritance Relation Guided One-Shot Layer Assignment Search (Meng et al. 2020)

Automatically Searching for U-Net Image Translator Architecture (Shu and Wang. 2020)

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (Zhao et al. 2020)

Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search (Hong et al. 2020; accepted at WACV’20 workshop)

Search for Winograd-Aware Quantized Networks (Fernandez-Marques et al. 2020)

Semi-Supervised Neural Architecture Search (Luo et al. 2020)

Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction (Yan et al. 2020)

DSNAS: Direct Neural Architecture Search without Parameter Retraining (Hu et al. 2020)

Neural Architecture Search For Fault Diagnosis (Li et al. 2020; accepted at ESREL’20)

Learning Architectures for Binary Networks (Singh et al. 2020)

Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB (Johner and Wassner. 2020; accepted at ICMLA’19)

Automating Deep Neural Network Model Selection for Edge Inference (Lu et al. 2020; accepted at CogMI’20)

Neural Architecture Search over Decentralized Data (Xu et al. 2020)

Automatic Structural Search for Multi-task Learning VALPs (Garciarena et al. 2020; accepted at OLA’20)

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning (Alletto et al. 2020; accepted at Meta-Eval 2020 workshop)

Classifying the classifier: dissecting the weight space of neural networks (Eilertsen et al. 2020)

Stabilizing Differentiable Architecture Search via Perturbation-based Regularization (Chen and Hsieh. 2020)

Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator (Abdelfattah et al. 2020; accepted at DAC’20)

Variational Depth Search in ResNets (Antoran et al. 2020)

Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks (Yang et al. 2020; accepted at DAC’20)

FPNet: Customized Convolutional Neural Network for FPGA Platforms (Yang et al. 2020; accepted at FPT’20)

AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning (Basha et al. 2020)

NASS: Optimizing Secure Inference via Neural Architecture Search (Bian et al. 2020; accepted at ECAI’20)

Search for Better Students to Learn Distilled Knowledge (Gu et al. 2020)

Bayesian Neural Architecture Search using A Training-Free Performance Metric (Camero et al. 2020)

NAS-Bench-1Shot1: Benchmarking and Dissecting One-Short Neural Architecture Search (Zela et al. 2020; accepted at ICLR’20)

Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification (Chen et al. 2010)

Multi-objective Neural Architecture Search via Non-stationary Policy Gradient (Chen et al. 2020)

Efficient Neural Architecture Search: A Broad Version (Ding et al. 2020)

ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel (Fan et al. 2020)

FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks (Iqbal et al. 2020)

Up to two billion times acceleration of scientific simulations with deep neural architecture search (Kasim et al. 2020)

Latency-Aware Differentiable Neural Architecture Search (Xu et al. 2020)

MixPath: A Unified Approach for One-shot Neural Architecture Search (Chu et al. 2020)

Neural Architecture Search for Skin Lesion Classification (Kwasigroch et al. 2020; accepted at IEEE Access)

AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search (Chen et al. 2020)

Neural Architecture Search for Deep Image Prior (Ho et al. 2020)

Fast Neural Network Adaptation via Parameter Remapping and Architecture Search (Fang et al. 2020; accepted at ICLR’20)

FTT-NAS: Discovering Fault-Tolerant Neural Architecture (Li et al. 2020; accepted at ASP-DAC 2020)

Deeper Insights into Weight Sharing in Neural Architecture Search (Zhang et al. 2020)

EcoNAS: Finding Proxies for Economical Neural Architecture Search (Zhou et al. 2020; accepted at CVPR’20)

DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems (Loni et al. 2020; accepted at Microprocessors and Microsystems)

Auto-ORVNet: Orientation-boosted Volumetric Neural Architecture Search for 3D Shape Classification (Ma et al. 2020; accepted at IEEE Access)

NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search (Dong and Yang et al. 2020; accepted at ICLR’20)

Deep Reinforcement Learning via Policy Optimization

The tutorial is written for those who would like an introduction to reinforcement learning (RL). The aim is to provide an intuitive presentation of the ideas rather than concentrate on the deeper mathematics underlying the topic. RL is generally used to solve the so-called Markov decision problem (MDP). In other words, the problem that you are attempting to solve with RL should be an MDP or its variant. The theory of RL relies on dynamic programming (DP) and artificial intelligence (AI). We will begin with a quick description of MDPs. We will discuss what we mean by “complex” and “large-scale” MDPs. Then we will explain why RL is needed to solve complex and large-scale MDPs. The semi-Markov decision problem (SMDP) will also be covered.

The tutorial is meant to serve as an introduction to these topics and is based mostly on the book: “Simulation-based optimization: Parametric Optimization techniques and reinforcement learning” [4]. The book discusses this topic in greater detail in the context of simulators. There are at least two other textbooks that I would recommend you to read: (i) Neuro-dynamic programming [2] (lots of details on convergence analysis) and (ii) Reinforcement Learning: An Introduction [11] (lots of details on underlying AI concepts). A more recent tutorial on this topic is [8]. This tutorial has 2 sections: ? Section 2 discusses MDPs and SMDPs. ? Section 3 discusses RL. By the end of this tutorial, you should be able to ? Identify problem structures that can be set up as MDPs / SMDPs. ? Use some RL algorithms.