For example, AlphaGo and AlphaGo Zero for game of GO (Silver et al. (2014) proposed Neural Turing Machine (NTM) architecture, consisting of a neural network controller and a memory bank. This course aims to provide an overview of the recent developments in RL combined with advances in deep learning. (2016) presented several methods for training GANs. Cheng-Yang Fu, and Alexander C. Berg. Antonoglou, Daan Wierstra, and Martin A. Riedmiller. It is often hard to keep track with contemporary advances in a research area, provided that field has great value in near future and related applications. Browse our catalogue of tasks and access state-of-the-art solutions. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn … David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John In early Auto-Encoders (AE), encoding layer had smaller dimensions than the input layer. In recent years, TNs have been increasingly investigated and applied to machine learning for high-dimensional data analysis, model compression and efficient computation in deep neural networks (DNNs), and theoretical analysis of expressive power for DNNs. Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, (2015), Peng and Yao (2015), Amodei et al. In this section, we will briefly discuss other deep architecures which uses multiple levels of abstraction and representation similar to deep neural networks, also known as Deep Generative Models (DGM). (2016) explained the basic CNN architecures and the ideas. DL approaches allow computers to learn compli- cated concepts by building them out of simpler ones (Goodfellow et al., 2016). (2017), Ranzato et al. Other techniques and neural networks came as well e.g. (2015)), Chess and Shougi (Silver et al., 2017a). Girshick et al. Each expert is the same architecture of fully connected layers from Fast R-CNN (Lee et al., 2017). Recent advances in convolutional neural networks. Input, Question, Episodic Memory, Output (Kumar et al., 2015). (2015) proposed Highway Networks, which uses gating units to learn regulating information through. (2017) proposed a CNN architecture for sequence-to-sequence learning. Driessche, Thore Graepel, and Demis Hassabis. Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep learning and quantum-inspired deep learning techniques in recent times. Generative visual manipulation on the natural image manifold. The briefed the models graphically along with the breakthroughs in DL research. Blocks and fuel: Frameworks for deep learning. Mastering the game of go without human knowledge. LSTMs. (2016c), Zhang et al. 07/09/2018 ∙ by Emilia Gómez, et al. attention. This architecture is composed of 29 convolution layers. (2015) presented a nice overview on recent advances of CNNs, multiple variants of CNN, its architectures, regularization methods and functionality, and applications in various fields. Wu et al. DOI: 10.1109/MSP.2017.2732900 Corpus ID: 6983001. The recent advances reported for this task have been showing that deep learning is the most successful machine learning … Every now and then, AI bots created with DNN and DRL, are beating human world champions and grandmasters in strategical and other games, from only hours of train- ing. ∙ Razvan Pascanu, Guillaume Desjardins, Joseph P. Turian, David Warde-Farley, van den Oord et al. David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Keeping up with the trend of many recent years, Deep Learning in 2020 continued to be one of the fastest-growing fields, darting straight ahead into the Future of Work. Convolutional layers detect local conjunctions from features and pooling layers merge similar features into one (LeCun et al., 2015). Demystifying alphago zero as alphago GAN. (2017) talked about DL models and architectures, mainly used in Natural Language Processing (NLP). Olah and Carter (2016) gave nice presentation of Attentional and Augmented Recurrent Neural Networks i.e. Recent advances in Deep Learning also incorporate ideas from statistical learning [1,2], reinforcement learning (RL) [3], and numerical optimization. LSTM is based on recurrent network along with gradient-based learning algorithm (Hochreiter and Schmidhuber, 1997) LSTM introduced self-loops to produce paths so that gradient can flow (Goodfellow et al., 2016). a discriminative model to learn model or data dis- tribution (Goodfellow et al., 2014). (2016) proposed another VDCNN architecture for text classification which uses small convolutions and pooling. (2015) proposed a Deep Generative Model (DGM) called Laplacian Generative Adversarial Networks (LAPGAN) using Generative Adversarial Networks (GAN) approach. (2014) proposed Dropout to prevent neural networks from overfitting. van Hasselt et al. Fractals are repeated architecture generated by simple expansion rule (Larsson et al., 2016). (2015)), document processing (Hinton and Salakhutdinov, 2011), character motion synthesis and editing (Holden et al., 2016), singing synthesis (Blaauw and Bonada, 2017), face recognition and verification (Taigman et al., 2014), action recognition in videos (Simonyan and Mnih et al. a discriminative model to learn model or data distribution. Yangyang Shi, Kaisheng Yao, Hu Chen, Dong Yu, Yi-Cheng Pan, and Mei-Yuh In this paper, firstly we will provide short descriptions of the past overview papers on deep learning models and approaches. Keywords: Neural Networks, Machine Learning, Deep Learning, Recent Advances, Overview. (2016) proposed Quasi Recurrent Neural Networks (QRNN) for neural sequence modelling, appling parallel across timesteps. He has spoken and written a lot about what deep learning is and is a good place to start. The model also uses convolutional networks within a Laplacian pyramid framework (Denton et al., 2015). Xie et al. Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across a large number of Google services.. Mnih et al. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Zhang et al. Zhang et al. Goodfellow et al. Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, and Ali Farhadi. An improvement of CapsNet is proposed with EM routing (Anonymous, 2018b). ∙ Facebook AI Research (FAIR). Deep learning methods are composed of multiple layers to learn features of data with multiple levels of abstraction (LeCun et al., 2015). (2015)), video classification (Karpathy et al., 2014), defect classification (Masci et al., 2013b), text, speech, image and video processing (LeCun et al., 2015), text classification (Conneau et al., 2016), speech recognition and spoken language understanding (Hinton et al. Hinton and Salakhutdinov (2011) proposed a Deep Generative Model using Restricted Boltzmann Machines (RBM) for document processing. Convolutional neural networks for sentence classification. Neural programmer: Inducing latent programs with gradient descent. Fujun Luan, Sylvain Paris, Eli Shechtman, and Kavita Bala. Srivastava et al. Using recurrent neural networks for slot filling in spoken language Learning to discover cross-domain relations with generative Resnet in resnet: Generalizing residual architectures. Tip: you can also follow us on Twitter ∙ 0 ∙ share . (2015)), Chess and Shougi (Silver et al., 2017a). DL has been solving many problems while taking technologies to another dimension. Exploring the limits of language modeling. Yichuan Tang, Ruslan Salakhutdinov, and Geoffrey Hinton. Our paper is mainly for the new learners and novice researchers who are new to this field. Google’s neural machine translation system: Bridging the gap between (2014), Xu et al. Dilek Z. Hakkani-Tür, Xiaodong He, Larry P. Heck, Gökhan By reviewing a large body of recent related work in literature, … DLN is a combination of lambertian reflectance with Gaussian Restricted Boltzmann Machines and Deep Belief Networks (Tang et al., 2012). Deutsch (2018) used Hyper Networks for generating neural networks. Bengio (2009) explained neural networks for deep architectures e.g. Deng and Yu (2014) briefed deep architectures for unsupervised learning and explained deep Autoencoders in detail. Max-Pooling Convolutional Neural Networks (MPCNN) operate on mainly convolutions and max-pooling, especially used in digital image processing. Ioffe and Szegedy (2015) proposed Batch Normalization, a method for accelerating deep neural network training by reducing internal covariate shift. This means we don't have a direct analogy to the notion of some unique set of weights that perform well on the task at hand. LeCun et al. (2016) proposed a DRL framework using asynchronous gradient descent for DNN optimization. ∙ Max-Pooling Convolutional Neural Networks (MPCNN) operate on mainly convolutions and max-pooling, especially used in digital image processing. description. Rich feature hierarchies for accurate object detection and semantic (2017b), Arik et al. Goodfellow et al. Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. Richard Zhang, Phillip Isola, and Alexei A. Efros. Read writing about Deep Learning in Recent Advances in Deep Learning: An Overview. (2014), Oquab et al. (2015)), named entity recognition (Lample et al., 2016), conversational agents (Ghazvininejad et al., 2017), calling genetic variants (Poplin et al., 2016), X-ray CT reconstruction (Kang et al., 2016). Also, Deep Learning (DL) models are immensely successful in Unsupervised, Hybrid and Reinforcement Learning as well (LeCun et al., 2015). ResNext exploits ResNets (He et al., 2015) for repeating layers with split-transform-merge strategy (Xie et al., 2016). Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep learning and quantum-inspired deep learning techniques in recent times. models. (2015)), document processing (Hinton and Salakhutdinov, 2011), character motion synthesis and editing (Holden et al., 2016), singing synthesis (Blaauw and Bonada, 2017), face recognition and verification (Taigman et al., 2014), action recognition in videos (Simonyan and Zisserman, 2014a), human action recognition (Ji et al., 2013), classifying and visualizing motion capture sequences (Cho and Chen, 2013), handwriting generation and prediction (Carter et al., 2016), automated and machine translation (Wu et al. Binhua Tang 1,2 * †, Zixiang Pan 1 †, Kang Yin 1 and Asif Khateeb 1. A deep learning framework for character motion synthesis and editing. Brian Kingsbury. Schmidhuber (2014) described advances of deep learning in Reinforce- ment Learning (RL) and uses of Deep Feedforward Neural Netowrk (FNN) and Recurrent Neural Network (RNN) for RL. Bansal et al. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sabour et al. 05/08/2020 ∙ by Siddhant Garg, et al. Goodfellow et al. Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. Efros. Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca Maria Gambardella, and Marcus (2018) thinks DL needs to be reconceptualized and to look for possibilities in unsupervised learning, symbol manipulation and hybrid models, having insights from cognitive science and psychology and taking bolder challenges. CapsNet usually contains several convolution layers and on capsule layer at the end (Xi et al., 2017). It is often hard to keep track with contemporary advances in a research area, provided that field has great value in near future and related applications. The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. (2015) proposed Neural Random Access Machine, which uses an external variable-size random-access memory. Bengio (2009) discussed deep architectures i.e. (2015)), text-to-speech generation (Wang et al. Deep learning methods have brought revolutionary advances in computer vision (2016), Variational Auto-Encoders (VAE) can be counted as decoders (Wang). share. The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Schmidhuber (2014) covered all neural networks starting from early neural networks to recently successful Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and their improvements. http://dx.doi.org/10.1109/CVPR.2011.5995710. Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. Yu Zhang, Guoguo Chen, Dong Yu, Kaisheng Yao, Sanjeev Khudanpur, and James R. (2017b), Silver et al. Deep learning for environmentally robust speech recognition: An (2016),?DBLP:journals/corr/AntolALMBZP15)), visual recognition and description (Donahue et al. In this blog post, we will cover some of the recent advances in optimization for gradient descent algorithms. Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. discuss about recent advances in Deep Learning for past few years. Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Tran, Bryan Catanzaro, and Evan Shelhamer. Attention and augmented recurrent neural networks. Ba et al. Bengio (2013) did quick overview on DL algorithms i.e. Goodfellow et al. Dally, and Kurt Keutzer. Recent advances in deep learning and transfer learning have resulted in breakthrough leaps in what’s newly achievable in natural language understanding (NLU). Show, attend and tell: Neural image caption generation with visual Jürgen Schmidhuber. All recent overview papers on Deep Learning (DL) discussed important things from several perspectives. Karpathy et al. Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. Deep Metric Learning for Visual Understanding: An Overview of Recent Advances Abstract: In this article, we have summarized the recent trends of DML and shown their wide applications of various visual understanding tasks including face recognition, image classification, visual search, person reidentification, visual tracking, cross-modal matching, and image set classification. (2012) presented a Deep Convolutional Neural Network (CNN) archi- tecture, also known as AlexNet, which was a major breakthrough in Deep Learning (DL). Large-scale video classification with convolutional neural networks. For Artificial Neural Networks (ANN), Deep Learning (DL) aka hierarchical learning (Deng and Yu, 2014) is about assigning credits in many computational stages accurately, to transform the aggregate activation of the network (Schmidhuber, 2014). A deep learning architecture comprising homogeneous cortical circuits Neelakantan et al. van den Oord et al. (2016), Dong et al. Batch renormalization: Towards reducing minibatch dependence in Recent trends in deep learning based natural language processing. Rigoll. (2017) proposed Fader Networks, a new type of encoder-decoder architecture to generate realistic variations of input images by changing attribute values. (2016), Dong et al. Understanding deep learning requires rethinking generalization. (2016a) proposed WaveNet, deep neural network for generating raw audio. (2017) proposed Mask Region-based Convolutional Network (Mask R-CNN) instance object segmentation. (2014) proposed a Deep CNN architecture named Inception. ∙ Overview papers are found to be very beneficial, especially for new researchers in a particular field. Artificial Neural Networks (ANN) have come a long way, as well as other deep models. (2017) proposed Variational Bi-LSTMs, which is a variant of Bidirectional LSTM architecture. Memory Networks are composed of memory, input feature map, generalization, output feature map and response (Weston et al., 2014) . Get the latest machine learning methods with code. Since 2009, Microsoft has engaged with academic pioneers of deep learning and has created industry-scale successes in speech recognition as well as in speech translation, object recognition, automatic image captioning, natural language, multimodal processing, semantic modeling, web search, contextual entity search, ad selection, and big data analytics. Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole CapsNet is considered as one of the most recent breakthrough in Deep Learning (Xi et al., 2017), since this is said to be build upon the limitations of Convolutional Neural Networks (Hinton, ). We hope deep learning and AI will be much more devoted to the betterment of humanity, to carry out the hardest scientific researches, and last but not the least, to make the world a more better place for every single human. (2012), Zhang et al. Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. networks. talked about DL models and architectures, mainly used in Natural Language Processing (NLP). Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo http://dl.acm.org/citation.cfm?id=1756006.1756030, http://www.scholarpedia.org/article/Deep_Learning. Then Support Vector Machine (SVM) surfaced, and surpassed ANNs for a while. Larochelle, Aaron C. Courville, and Chris Pal. FractalNet, as an alternative to residual nets. R-CNN uses regions to localize and segment objects. share, Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation... (2015) proposed Dynamic Memory Networks (DMN) for QA tasks. It augments con- volutional residual networks with a long short term memory mechanism (Moniz and Pal, 2016). Classifying and visualizing motion capture sequences using deep A Recurrent hidden unit can be considered as very deep feedforward network with same weights when unfolded in time. Graves et al. Many improvements were proposed later to solve this problem. Where: Amsterdam, Netherlands. Ended. The auxiliary variables make variational distribution with stochastic layers and skip connections (Maaløe et al., 2016). The network composed of five convolutional layers and three fully connected layers. David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew (2015) proposed a CNN architecture named YOLO (You Only Look Once) for unified and real-time object detection. Iandola et al. Li (2017) discussed Deep Reinforcement Learning(DRL), its architectures e.g. And fully-connected layers does the linear multiplication (Masci et al., 2013a). Girshick (2015) proposed Fast Region-based Convolutional Network (Fast R-CNN). NIN replaces convolution layers of traditional Convolutional Neural Network (CNN) by micro neural networks with complex structures. Goodfellow et al. Very deep convolutional networks for large-scale image recognition. Learning phrase representations using RNN encoder-decoder for Sukthankar, and Li Fei-Fei. learning. Capsule network performance on complex data. Deep neural networks for acoustic modeling in speech recognition. (2013) discussed on Representation and Feature Learning aka Deep Learning. Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Deep reinforcement learning with double q-learning. statistical machine translation. Chris Dyer. Deep learning is a new area of machine learning research, which have demonstrated states-of-the-art performance on many artificial intelligence tasks, e.g., computer vision, speech recognition and natural language processing. internal covariate shift. (2015a), Shi et al. Marcus (2018) thinks DL needs to be reconceptualized and to look for possibilities in unsupervised learning, symbol manipulation and hybrid models, having insights from cognitive science and psychology and taking bolder challenges. (2015), van Hasselt et al. Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc V. First generation of ANNs was composed of simple neural layers for Perceptron. Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, 8 Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. Also it uses per-RoI multi-expert network instead of single per-RoI network. What’s next When first published in August 2018, the CoQA baseline automated system had an F1 score of 65.4%, well below the human performance of 88.8%. (2016) proposed Auxiliary Deep Generative Models where they extended Deep Generative Models with auxiliary variables. Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, This paper is an overview of most recent techniques of deep learning, mainly recommended for upcoming researchers in this field. Neural Turing Machines (NTM), Attentional Interfaces, Neural Pro- grammer and Adaptive Computation Time. Considering the popularity and expansion of Deep Learning in recent years, we present a brief overview of Deep Learning as well as Neural Networks (NN), and its major advances and critical breakthroughs from past few years. (2017) proposed Recurrent Highway Networks (RHN), which extend Long Short-Term Memory (LSTM) architecture. Playing atari with deep reinforcement learning. (2017a) described the evolution of deep learning models in time-series man- ner. James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, (2015) proposed a Deep Generative Model (DGM) called Laplacian Generative Adversarial Networks (LAPGAN) using Generative Adversarial Networks (GAN) approach. NTMs usually combine RNNs with external memory bank (Olah and Carter, 2016). (2015) proposed Faster Region-based Convolutional Neural Networks (Faster R-CNN), which uses Region Proposal Network (RPN) for real-time object detection. Recent advances in computer vision have made accurate, fast and robust Very deep convolutional networks for text classification. We hope that this paper will help many novice researchers in this field, getting an overall picture of recent Deep Learning researches and techniques, and guiding them to the right way to start with. Deng and Yu (2014) detailed some neural network architectures e.g. Learning useful representations with little or no supervision is a key challenge in artificial intelligence. In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years. Peng and Yao (2015) proposed Recurrent Neural Networks with External Memory (RNN-EM) to improve memory capacity of RNNs. (2016) proposed ResNeXt architecture. Most of them are built for python programming language. Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. They were limited in simples computations. Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. An intuitive overview of recent advances in automated reading comprehension, Part I. Ioffe (2017) proposed Batch Renormalization extending the previous approach. He et al. Thomas Rückstieß, and Jürgen Schmidhuber. In recent years, the world has seen many major breakthroughs in this field. Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Donahue et al. neural networks into compressed and smaller model. Dropout is a neural network model-averaging regularization method by adding noise to its hidden units. (2015) published a overview of Deep Learning (DL) models with Convo- lutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Restricted Boltzmann Machines (RBM) are special type of Markov random field containing one layer of stochastic hidden units i.e. Zhang et al. MPCNN generally consists of three types of layers other than the input layer. Advances and Applications in Deep Learning: an overview. Goodfellow et al. He also mentioned that DL assumes stable world, works as approximation, is difficult to engineer and has potential risks as being an excessive hype. Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. Tür, Dong Yu, and Geoffrey Zweig. Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. Srivastava et al. (2016) discussed deep networks and generative models in details. (2014), Razavian et al. We tested this agent on the challenging domain of … (2015) provided a brief yet very good explanation of supervised learning approach and how deep architectures are formed. Nielsen (2015) described the neural networks in details along with codes and examples. Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A. Autoencoders (AE) are neural networks (NN) where outputs are the inputs. RHNs use Highway layers inside the recurrent transition (Zilly et al., 2017). (2015) proposed Deep Residual Learning framework for Deep Neural Networks (DNN), which are called ResNets with lower training error (He, ). It is necessary to go through them for a DL researcher. Overview: Advances in machine learning are continuously penetrating computational science and engineering. Tang et al. (2016) proposed Zoneout, a regularization method for Recurrent Neural Networks (RNN). (2015) proposed Gated Feedback Recurrent Neural Networks (GF-RNN), which extends the standard RNN by stacking multiple recurrent layers with global gating units. adversarial networks. Karol Kurach, Marcin Andrychowicz, and Ilya Sutskever. In this section, we will provide short overview on some major techniques for regularization and optimization of Deep Neural Networks (DNN). First generation of ANNs was composed of simple neural layers for Perceptron. Simonyan and Zisserman (2014b) proposed Very Deep Convolutional Neural Network (VD- CNN) architecture, also known as VGG Nets. Zisserman, 2014a), human action recognition (Ji et al., 2013), classifying and visualizing motion capture sequences (Cho and Chen, 2013), handwriting generation and prediction (Carter et al., 2016), automated and machine translation (Wu et al. (2016) developed a class for one-shot generalization of deep generative models. Moniz and Pal (2016) proposed Convolutional Residual Memory Networks, which incor- porates memory mechanism into Convolutional Neural Networks (CNN). Kavukcuoglu. speech recognition, handwriting recognition, and polyphonic music modeling. Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. (2011) built a deep generative model using Deep Belief Network (DBN) for images recognition. Theano: A cpu and gpu math compiler in python. Lample et al. Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. Huang et al. Deep learning techniques currently achieve state of the art performance in a multitude of problem domains (vision, audio, robotics, natural language processing, to name a few). Zilly et al. Finally, we will discuss about current status and the future of Deep Learning in the last two sections i.e. proposal networks. LSTM is based on recurrent network along with gradient-based learning algorithm (Hochreiter and Schmidhuber, 1997) LSTM introduced self-loops to produce paths so that gradient can flow (Goodfellow et al., 2016). There are other issues like transferability of features learned (Yosinski et al., 2014). (2015) proposed Neural Random Access Machine, which uses an external variable-size random-access memory. (2017) proposed an architecture for adersarial attacks on neural networks, where they think future works are needed for defenses against those attacks. Introduction This is a free seminar hosted by the IEEE Computer Society chapter, with thanks to MIcrosoft for the venue. Deep learning is becoming a mainstream technology for speech recognition at industrial scale. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. processing. Deep Metric Learning for Visual Understanding: An Overview of Recent Advances @article{Lu2017DeepML, title={Deep Metric Learning for Visual Understanding: An Overview of Recent Advances}, author={Jiwen Lu and J. Hu and J. Zhou}, journal={IEEE Signal Processing Magazine}, year={2017}, volume={34}, pages={76-84} } (2016) discussed deep networks and generative models in details. (2017), Ranzato et al. (2014) proposed a Deep CNN architecture named Inception. Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. Deep NIN architectures can be made from multi-stacking of this proposed NIN structure (Lin et al., 2013). (2014)), object detection (Lee et al. Starting from Machine Learning (ML) basics, pros and cons for deep architectures, they concluded recent DL researches and applications thoroughly. Rezende et al. ∙ Visualizing and understanding recurrent networks. (2016), Variational Auto-Encoders (VAE) can be counted as decoders (Wang, ), . • Applicability of RL to multi-stage decision problems in industries is discussed. There were many overview papers on Deep Learning (DL) in the past years. J ́ozefowicz et al. It is also one of the most popular scientific research trends now-a-days. Deep Learning to the Rescue. (2017) proposed Capsule Networks (CapsNet), an architecture with two convo- lutional layers and one fully connected layer. 09/30/2019 ∙ by Mackenzie W. Mathis, et al. Shan Carter, David Ha, Ian Johnson, and Chris Olah. However, there are many difficult problems for humanity to deal with. (2016a), Mesnil et al. A Recurrent hidden unit can be considered as very deep feedforward network with same weights when unfolded in time. Deep architectures are multilayer non-linear repetition of simple architectures in most of the cases, which helps to obtain highly complex functions out of the inputs (LeCun et al., 2015). We are still away from fully understanding of how deep learning works, how we can get machines more smarter, close to or smarter than humans, or learning exactly like human. He also discussed deep neural networks and deep learning to some extent. Zisserman (2014b) proposed Very Deep Convolutional Neural Network (VDCNN) architecture, also known as VGG Nets. Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous. (2015), van Hasselt et al. Deep learning methods have brought revolutionary advances in (2016) explained deep generative models in details e.g. Neelakantan et al. Convolutional Neural Networks (CNN), Auto-Encoders (AE) etc. They described DL from the perspective of Representation Learning, showing how DL techniques work and getting used successfully in various applications, and predicting future learning based on Unsupervised Learning (UL). However, a drawback of machine learning algorithms is that their performance depends on human engineering. In Stacked Denoising Auto-Encoders (SDAE), encoding layer is wider than the input layer (Deng and Yu, 2014).