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I have posted this earlier in LinkedIn.TL;DR - Yes it is, network economy will finally disrupt the enterprise software.When we hear the term enterprise software today, in early 2016, we most likely think of one of the most popular cloud-based SaaS (Software-as-a-Service) applications. Your company probably uses SaaS apps such as Salesforce, Hubspot, Zendesk, Slack, Workday and Zenefits. The largest “blue chip” enterprises often still run traditional, privately hosted apps from their fellow blue chip vendors like Microsoft, IBM, SAP or Oracle.All of these enterprise software apps have the same basic licensing and data ownership model. The application is licensed by a company for its employees, and the licensee company owns every single bit of the data that is saved to the application by its employees. Some applications let companies gather additional data from 3rd parties via so-called “guest accounts” that offer restricted access to the company records. In these cases too, all of the information added to the system is owned by the licensee of the application.The problem with this model is that there is less and less interest among professionals to learn to use company-issued software suites. Not only are they often cumbersome and complex, but in addition, all data you ever enter to these apps is left behind the instant you move on. It is like an opaque silo, into which you submit your data. Because you don’t personally gain any direct and instant benefits from it, you have very little incentive in maintaining and improving such data. Enterprise software vendors, as well as their customer companies, have been struggling with user engagement and how to incentivize the users to add sufficient information to these systems in a timely fashion.We live in a network economyWhat makes things worse for the enterprise software today is that we live in a network economy. According to Wikipedia, network economy is “the emerging economic order within the information society. The name stems from a key attribute - products and services are created and value is added through social networks operating on large or global scales. This is in sharp contrast to industrial-era economies, in which ownership of physical or intellectual property stems from its development by a single enterprise.”This means the contributors of value to your business are less inside, and more outside of the company boundaries. It is common for companies to leverage service providers, freelancers, contractors, consultants, agents and suppliers at all levels of operation. In addition, employment is more fluid than ever – people change jobs and move between companies at an accelerating pace.This is good news for agile startups that find innovative ways to leverage supplier networks for growth and cost savings. It is now easier than ever to build a business with a minimal team. You simply source everything outside your core competence area via your network. And with ever expanding networks, it is increasingly easy to find the best socially validated provider for every need, independent of where the provider is located.The following table should make a convincing case that the network economy has boosted the company values per employee significantly for younger companies that never had to hire for many of its functions due to the value created by ecosystem and members in their network. The biggest value per employee resides in pure-play networks such as Whatsapp, Snapchat and Facebook. The second tier in this sample – Alphabet, Apple and Microsoft – have all partially benefited from the network economy. The bottom three samples are juggernauts of the past. Cisco, Intel and IBM built their business on their own, and never had a boost from the network economy.Consumerization empowers the end usersThe network economy drives consumerization of information technology in the enterprise. Wikipedia says: “Consumerization is the reorientation of product and service designs to focus on (and market to) the end user as an individual consumer, in contrast with an earlier era of only organization-oriented offerings”. When we discuss about consumerization of IT, we usually refer to BYOD (bring your own device) in mobile, and to the use of consumer services such as Dropbox and Gmail for work-related tasks. But it goes much deeper than that.In a network economy, every user of your app or service is a source of valuable data that helps your company build a better business. When they rate your app or invite their friends, they are a valuable referral sources. When they indicate an interest in something with their behavior, they become potential customers of the companies that serve such interests. When they join a company, they may become an ambassador to your product or service in that company. When they switch to a competitor product, they may help them compete better with your company by pointing out your weaknesses.In such an environment, the only reasonable goal for any app developer is to make their end users raving fans of their product or service. Let it sink in for a moment – you should focus on serving your users instead of the entity that pays for the service. If the point of your enterprise app is to increase the productivity of engineers, it is not cool anymore that you provide a tracking system for managers that force their engineers to commit more code. Instead, you must find ways to make the engineers feel more productive and happy using your product. Accordingly, if the point of your software is to help salespeople sell more, it is not cool anymore to force them make more calls per day with an automated calling system. While that may make their bosses happy, the salespeople themselves will feel unhappy and treated like robots. Instead, you need to find ways to make salespeople feel more productive in their own preferred style of working.Network economy is dynamic and fragmentedIn a network economy, employees and other value contributors move freely and frequently. This makes it especially important for businesses to minimize the time to onboard new employees and other contributors. If the employers let their value contributors leverage the apps and tools they already know and love, they can be productive from day one.Likewise, for employees to quickly demonstrate their value and be successful, it is crucial that they can leverage what they have already learned when starting a new assignment. Especially contractors, who tend to move between projects frequently, can’t afford to bill their customers for learning new tools. It is much more productive for both parties that the contractors bring their own tools.Because ideas and trends move rapidly in a network, the priority has shifted from bringing a big group of people to an integrated environment with standard tools and processes to a distributed environment where the company leverages the best tools and resources from all over the world for every specific task that needs to be done. Specialized apps can solve specific problems in a much faster, simpler and cheaper way than a large enterprise software suite.How to succeed in network economy as an enterprise software developerIf you develop software for businesses, here are four concrete steps to make your company successful in a network economy. This list is by no means complete. There are many more things to consider, but these four will get you started in a meaningful way.Let everyone work with their favorite apps. In a network economy, professionals make their own choices and decisions, leading to consumerization of IT. Bring your own device, BYOD, has accelerated this trend. With millions of apps available, everyone is, and should be, able to make their own preferred choice. Freedom to use our favorite apps and productivity tools makes us happy. You should offer your service so that it does not force your users to standardize on a single app.Help your users leverage their network. In order to capture the important communication data and business insights from your users’ networks, you need to provide a simple access to that network. Don’t limit the collaboration to a team inside a single company, like the traditional enterprise software vendors do. Also, make sure to always offer a free version of your app to enable collaboration with anyone. Slack does this already by letting their users join multiple teams, but even that is a limited approach. Dropbox model of sharing anything with anyone is even better.Focus on the end users and their needs. As I argued before, this is the key to be successful in network economy. First and foremost, you need to delight your end users – everything else is secondary. When traditional enterprise software vendors offer forms to fill and tools to use, you should think how to remove that work altogether. Filling forms does not serve the needs of your users, only their employers.Sell services and business insights - not tools. For your customers, the millions of already available and constantly improving apps are already the tools they use and are happy with. Most of these apps offer a free tier of usage. Many professionals today choose to pay personally for premium services within these apps rather than using their employers’ enterprise suites. To sell to this group of professionals, or their employers, you need to focus on delivering a service that makes their lives easier and better. Service that offers real, tangible value, and removes work from the professionals. You should be the assistant they hire, not the tool they use.Disclosure: My startup Inbot develops an AI-powered assistant that is optimized for the network economy.

You get through this list: Awesome Deep Learning ResourcesThis is a rough list of my favorite deep learning resources. It has been useful to me for learning how to do deep learning, I use it for revisiting topics or for reference. I (Guillaume Chevalier) have built this list and got through all of the content listed here, carefully.ContentsTrendsOnline classesBooksPosts and ArticlesPractical resourcesLibrairies and ImplementationsSome DatasetsOther Math TheoryGradient Descent Algorithms and optimizationComplex Numbers & Digital Signal ProcessingPapersRecurrent Neural NetworksConvolutional Neural NetworksAttention MechanismsOtherYouTube and VideosMisc. Hubs and LinksLicenseTrendsHere are the all-time Google Trends, from 2004 up to now, September 2017:You might also want to look at Andrej Karpathy's new post about trends in Machine Learning research.I believe that Deep learning is the key to make computers think more like humans, and has a lot of potential. Some hard automation tasks can be solved easily with that while this was impossible to achieve earlier with classical algorithms.Moore's Law about exponential progress rates in computer science hardware is now more affecting GPUs than CPUs because of physical limits on how tiny an atomic transistor can be. We are shifting toward parallel architectures [read more]. Deep learning exploits parallel architectures as such under the hood by using GPUs. On top of that, deep learning algorithms may use Quantum Computing and apply to machine-brain interfaces in the future.I find that the key of intelligence and cognition is a very interesting subject to explore and is not yet well understood. Those technologies are promising.Online ClassesMachine Learning by Andrew Ng on Coursera - Renown entry-level online class with certificate. Taught by: Andrew Ng, Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera.Deep Learning Specialization by Andrew Ng on Coursera - New series of 5 Deep Learning courses by Andrew Ng, now with Python rather than Matlab/Octave, and which leads to a specialization certificate.Deep Learning by Google - Good intermediate to advanced-level course covering high-level deep learning concepts, I found it helps to get creative once the basics are acquired.Machine Learning for Trading by Georgia Tech - Interesting class for acquiring basic knowledge of machine learning applied to trading and some AI and finance concepts. I especially liked the section on Q-Learning.Neural networks class by Hugo Larochelle, Université de Sherbrooke - Interesting class about neural networks available online for free by Hugo Larochelle, yet I have watched a few of those videos.GLO-4030/7030 Apprentissage par réseaux de neurones profonds - This is a class given by Philippe Giguère, Professor at University Laval. I especially found awesome its rare visualization of the multi-head attention mechanism, which can be contemplated at the slide 28 of week 13's class.BooksHow to Create a Mind - The audio version is nice to listen to while commuting. This book is motivating about reverse-engineering the mind and thinking on how to code AI.Neural Networks and Deep Learning - This book covers many of the core concepts behind neural networks and deep learning.Deep Learning - An MIT Press book - Yet halfway through the book, it contains satisfying math content on how to think about actual deep learning.Some other books I have read - Some books listed here are less related to deep learning but are still somehow relevant to this list.Posts and ArticlesPredictions made by Ray Kurzweil - List of mid to long term futuristic predictions made by Ray Kurzweil.The Unreasonable Effectiveness of Recurrent Neural Networks - MUST READ post by Andrej Karpathy - this is what motivated me to learn RNNs, it demonstrates what it can achieve in the most basic form of NLP.Neural Networks, Manifolds, and Topology - Fresh look on how neurons map information.Understanding LSTM Networks - Explains the LSTM cells' inner workings, plus, it has interesting links in conclusion.Attention and Augmented Recurrent Neural Networks - Interesting for visual animations, it is a nice intro to attention mechanisms as an example.Recommending music on Spotify with deep learning - Awesome for doing clustering on audio - post by an intern at Spotify.Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source - Parsey McParseface's birth, a neural syntax tree parser.Improving Inception and Image Classification in TensorFlow - Very interesting CNN architecture (e.g.: the inception-style convolutional layers is promising and efficient in terms of reducing the number of parameters).WaveNet: A Generative Model for Raw Audio - Realistic talking machines: perfect voice generation.François Chollet's Twitter - Author of Keras - has interesting Twitter posts and innovative ideas.Neuralink and the Brain’s Magical Future - Thought provoking article about the future of the brain and brain-computer interfaces.Migrating to Git LFS for Developing Deep Learning Applications with Large Files - Easily manage huge files in your private Git projects.The future of deep learning - François Chollet's thoughts on the future of deep learning.Discover structure behind data with decision trees - Grow decision trees and visualize them, infer the hidden logic behind data.Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters - Learn to slay down hyperparameter spaces automatically rather than by hand.Estimating an Optimal Learning Rate For a Deep Neural Network - Clever trick to estimate an optimal learning rate prior any single full training.The Annotated Transformer - Good for understanding the "Attention Is All You Need" (AIAYN) paper.The Illustrated Transformer - Also good for understanding the "Attention Is All You Need" (AIAYN) paper.Improving Language Understanding with Unsupervised Learning - SOTA across many NLP tasks from unsupervised pretraining on huge corpus.NLP's ImageNet moment has arrived - All hail NLP's ImageNet moment.The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) - Understand the different approaches used for NLP's ImageNet moment.Uncle Bob's Principles Of OOD - Not only the SOLID principles are needed for doing clean code, but the furtherless known REP, CCP, CRP, ADP, SDP and SAP principles are very important for developping huge software that must be bundled in different separated packages.Practical ResourcesLibrairies and ImplementationsNeuraxle, a framwework for machine learning pipelines - The best framework for structuring and deploying your machine learning projects, and which is also compatible with most framework (e.g.: Scikit-Learn, TensorFlow, PyTorch, Keras, and so forth).TensorFlow's GitHub repository - Most known deep learning framework, both high-level and low-level while staying flexible.skflow - TensorFlow wrapper à la scikit-learn.Keras - Keras is another intersting deep learning framework like TensorFlow, it is mostly high-level.carpedm20's repositories - Many interesting neural network architectures are implemented by the Korean guy Taehoon Kim, A.K.A. carpedm20.carpedm20/NTM-tensorflow - Neural Turing Machine TensorFlow implementation.Deep learning for lazybones - Transfer learning tutorial in TensorFlow for vision from high-level embeddings of a pretrained CNN, AlexNet 2012.LSTM for Human Activity Recognition (HAR) - Tutorial of mine on using LSTMs on time series for classification.Deep stacked residual bidirectional LSTMs for HAR - Improvements on the previous project.Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Prediction - Tutorial of mine on how to predict temporal sequences of numbers - that may be multichannel.Hyperopt for a Keras CNN on CIFAR-100 - Auto (meta) optimizing a neural net (and its architecture) on the CIFAR-100 dataset.ML / DL repositories I starred - GitHub is full of nice code samples & projects.Smoothly Blend Image Patches - Smooth patch merger for semantic segmentation with a U-Net.Self Governing Neural Networks (SGNN): the Projection Layer - With this, you can use words in your deep learning models without training nor loading embeddings.Neuraxle - Neuraxle is a Machine Learning (ML) library for building neat pipelines, providing the right abstractions to both ease research, development, and deployment of your ML applications.Some DatasetsThose are resources I have found that seems interesting to develop models onto.UCI Machine Learning Repository - TONS of datasets for ML.Cornell Movie--Dialogs Corpus - This could be used for a chatbot.SQuAD The Stanford Question Answering Dataset - Question answering dataset that can be explored online, and a list of models performing well on that dataset.LibriSpeech ASR corpus - Huge free English speech dataset with balanced genders and speakers, that seems to be of high quality.Awesome Public Datasets - An awesome list of public datasets.SentEval: An Evaluation Toolkit for Universal Sentence Representations - A Python framework to benchmark your sentence representations on many datasets (NLP tasks).ParlAI: A Dialog Research Software Platform - Another Python framework to benchmark your sentence representations on many datasets (NLP tasks).Other Math TheoryGradient Descent Algorithms & Optimization TheoryNeural Networks and Deep Learning, ch.2 - Overview on how does the backpropagation algorithm works.Neural Networks and Deep Learning, ch.4 - A visual proof that neural nets can compute any function.Yes you should understand backprop - Exposing backprop's caveats and the importance of knowing that while training models.Artificial Neural Networks: Mathematics of Backpropagation - Picturing backprop, mathematically.Deep Learning Lecture 12: Recurrent Neural Nets and LSTMs - Unfolding of RNN graphs is explained properly, and potential problems about gradient descent algorithms are exposed.Gradient descent algorithms in a saddle point - Visualize how different optimizers interacts with a saddle points.Gradient descent algorithms in an almost flat landscape - Visualize how different optimizers interacts with an almost flat landscape.Gradient Descent - Okay, I already listed Andrew NG's Coursera class above, but this video especially is quite pertinent as an introduction and defines the gradient descent algorithm.Gradient Descent: Intuition - What follows from the previous video: now add intuition.Gradient Descent in Practice 2: Learning Rate - How to adjust the learning rate of a neural network.The Problem of Overfitting - A good explanation of overfitting and how to address that problem.Diagnosing Bias vs Variance - Understanding bias and variance in the predictions of a neural net and how to address those problems.Self-Normalizing Neural Networks - Appearance of the incredible SELU activation function.Learning to learn by gradient descent by gradient descent - RNN as an optimizer: introducing the L2L optimizer, a meta-neural network.Complex Numbers & Digital Signal ProcessingOkay, signal processing might not be directly related to deep learning, but studying it is interesting to have more intuition in developing neural architectures based on signal.Window Functions - Wikipedia page that lists some of the known window functions - note that the Hann-Poisson window is specially interesting for greedy hill-climbing algorithms (like gradient descent for example).MathBox, Tools for Thought Graphical Algebra and Fourier Analysis - New look on Fourier analysis.How to Fold a Julia Fractal - Animations dealing with complex numbers and wave equations.Animate Your Way to Glory, Math and Physics in Motion - Convergence methods in physic engines, and applied to interaction design.Animate Your Way to Glory - Part II, Math and Physics in Motion - Nice animations for rotation and rotation interpolation with Quaternions, a mathematical object for handling 3D rotations.Filtering signal, plotting the STFT and the Laplace transform - Simple Python demo on signal processing.PapersRecurrent Neural NetworksDeep Learning in Neural Networks: An Overview - You_Again's summary/overview of deep learning, mostly about RNNs.Bidirectional Recurrent Neural Networks - Better classifications with RNNs with bidirectional scanning on the time axis.Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation - Two networks in one combined into a seq2seq (sequence to sequence) Encoder-Decoder architecture. RNN Encoder–Decoder with 1000 hidden units. Adadelta optimizer.Sequence to Sequence Learning with Neural Networks - 4 stacked LSTM cells of 1000 hidden size with reversed input sentences, and with beam search, on the WMT’14 English to French dataset.Exploring the Limits of Language Modeling - Nice recursive models using word-level LSTMs on top of a character-level CNN using an overkill amount of GPU power.Neural Machine Translation and Sequence-to-sequence Models: A Tutorial - Interesting overview of the subject of NMT, I mostly read part 8 about RNNs with attention as a refresher.Exploring the Depths of Recurrent Neural Networks with Stochastic Residual Learning - Basically, residual connections can be better than stacked RNNs in the presented case of sentiment analysis.Pixel Recurrent Neural Networks - Nice for photoshop-like "content aware fill" to fill missing patches in images.Adaptive Computation Time for Recurrent Neural Networks - Let RNNs decide how long they compute. I would love to see how well would it combines to Neural Turing Machines. Interesting interactive visualizations on the subject can be found here.Convolutional Neural NetworksWhat is the Best Multi-Stage Architecture for Object Recognition? - Awesome for the use of "local contrast normalization".ImageNet Classification with Deep Convolutional Neural Networks - AlexNet, 2012 ILSVRC, breakthrough of the ReLU activation function.Visualizing and Understanding Convolutional Networks - For the "deconvnet layer".Fast and Accurate Deep Network Learning by Exponential Linear Units - ELU activation function for CIFAR vision tasks.Very Deep Convolutional Networks for Large-Scale Image Recognition - Interesting idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with just a few parameters. There is also a nice table for "ConvNet Configuration".Going Deeper with Convolutions - GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth.Highway Networks - Highway networks: residual connections.Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift - Batch normalization (BN): to normalize a layer's output by also summing over the entire batch, and then performing a linear rescaling and shifting of a certain trainable amount.U-Net: Convolutional Networks for Biomedical Image Segmentation - The U-Net is an encoder-decoder CNN that also has skip-connections, good for image segmentation at a per-pixel level.Deep Residual Learning for Image Recognition - Very deep residual layers with batch normalization layers - a.k.a. "how to overfit any vision dataset with too many layers and make any vision model work properly at recognition given enough data".Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning - For improving GoogLeNet with residual connections.WaveNet: a Generative Model for Raw Audio - Epic raw voice/music generation with new architectures based on dilated causal convolutions to capture more audio length.Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling - 3D-GANs for 3D model generation and fun 3D furniture arithmetics from embeddings (think like word2vec word arithmetics with 3D furniture representations).Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour - Incredibly fast distributed training of a CNN.Densely Connected Convolutional Networks - Best Paper Award at CVPR 2017, yielding improvements on state-of-the-art performances on CIFAR-10, CIFAR-100 and SVHN datasets, this new neural network architecture is named DenseNet.The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation - Merges the ideas of the U-Net and the DenseNet, this new neural network is especially good for huge datasets in image segmentation.Prototypical Networks for Few-shot Learning - Use a distance metric in the loss to determine to which class does an object belongs to from a few examples.Attention MechanismsNeural Machine Translation by Jointly Learning to Align and Translate - Attention mechanism for LSTMs! Mostly, figures and formulas and their explanations revealed to be useful to me. I gave a talk on that paper here.Neural Turing Machines - Outstanding for letting a neural network learn an algorithm with seemingly good generalization over long time dependencies. Sequences recall problem.Show, Attend and Tell: Neural Image Caption Generation with Visual Attention - LSTMs' attention mechanisms on CNNs feature maps does wonders.Teaching Machines to Read and Comprehend - A very interesting and creative work about textual question answering, what a breakthrough, there is something to do with that.Effective Approaches to Attention-based Neural Machine Translation - Exploring different approaches to attention mechanisms.Matching Networks for One Shot Learning - Interesting way of doing one-shot learning with low-data by using an attention mechanism and a query to compare an image to other images for classification.Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation - In 2016: stacked residual LSTMs with attention mechanisms on encoder/decoder are the best for NMT (Neural Machine Translation).Hybrid computing using a neural network with dynamic external memory - Improvements on differentiable memory based on NTMs: now it is the Differentiable Neural Computer (DNC).Massive Exploration of Neural Machine Translation Architectures - That yields intuition about the boundaries of what works for doing NMT within a framed seq2seq problem formulation.Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions - A WaveNet used as a vocoder can be conditioned on generated Mel Spectrograms from the Tacotron 2 LSTM neural network with attention to generate neat audio from text.Attention Is All You Need (AIAYN) - Introducing multi-head self-attention neural networks with positional encoding to do sentence-level NLP without any RNN nor CNN - this paper is a must-read (also see this explanation and this visualization of the paper).OtherProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections - Replace word embeddings by word projections in your deep neural networks, which doesn't require a pre-extracted dictionnary nor storing embedding matrices.Self-Governing Neural Networks for On-Device Short Text Classification - This paper is the sequel to the ProjectionNet just above. The SGNN is elaborated on the ProjectionNet, and the optimizations are detailed more in-depth (also see my attempt to reproduce the paper in code and watch the talks' recording).Matching Networks for One Shot Learning - Classify a new example from a list of other examples (without definitive categories) and with low-data per classification task, but lots of data for lots of similar classification tasks - it seems better than siamese networks. To sum up: with Matching Networks, you can optimize directly for a cosine similarity between examples (like a self-attention product would match) which is passed to the softmax directly. I guess that Matching Networks could probably be used as with negative-sampling softmax training in word2vec's CBOW or Skip-gram without having to do any context embedding lookups.YouTube and VideosAttention Mechanisms in Recurrent Neural Networks (RNNs) - IGGG - A talk for a reading group on attention mechanisms (Paper: Neural Machine Translation by Jointly Learning to Align and Translate).Tensor Calculus and the Calculus of Moving Surfaces - Generalize properly how Tensors work, yet just watching a few videos already helps a lot to grasp the concepts.Deep Learning & Machine Learning (Advanced topics) - A list of videos about deep learning that I found interesting or useful, this is a mix of a bit of everything.Signal Processing Playlist - A YouTube playlist I composed about DFT/FFT, STFT and the Laplace transform - I was mad about my software engineering bachelor not including signal processing classes (except a bit in the quantum physics class).Computer Science - Yet another YouTube playlist I composed, this time about various CS topics.Siraj's Channel - Siraj has entertaining, fast-paced video tutorials about deep learning.Two Minute Papers' Channel - Interesting and shallow overview of some research papers, for example about WaveNet or Neural Style Transfer.Geoffrey Hinton interview - Andrew Ng interviews Geoffrey Hinton, who talks about his research and breaktroughs, and gives advice for students.Misc. Hubs & LinksHacker News - Maybe how I discovered ML - Interesting trends appear on that site way before they get to be a big deal.DataTau - This is a hub similar to Hacker News, but specific to data science.Naver - This is a Korean search engine - best used with Google Translate, ironically. Surprisingly, sometimes deep learning search results and comprehensible advanced math content shows up more easily there than on Google search.Arxiv Sanity Preserver - arXiv browser with TF/IDF features.LicenseTo the extent possible under law, Guillaume Chevalier has waived all copyright and related or neighboring rights to this work.

Global Smart Waste Management System Market is expected to grow at a significant CAGR in the upcoming years as the scope, product types and its applications are increasing across the globe. Smart Waste Management System is a collective process for management of solid waste from residential and commercial societies, public place, streets, hospitals, and the other institutions. The initiation of advanced policies such as disposable tags, RFID, containers, and vacuum cleaners with a real-time dimension of wastes has established an important market for solid waste management. Furthermore, the rising concerns affecting the environmental hazards of land filling and incorrect removal of waste have also driven smarter ways of disposing of waste.Smart Waste Management System Market is categorized based on solutions, services, and geography. The Industry is categorized based on solutions such as Optimization Solutions, Asset Management, Analytics and Reporting Solutions, Network Management, Others. This Market is categorized based on services into Professional Services; Managed Services. Smart Waste Management System Industry is categorized based on Applications into Food & Retail, Construction, Manufacturing & Industrial, HealthCare, Municipalities, Colleges & UniversitiesRequest a Sample Copy of This Report @ https://www.millioninsights.com/industry-reports/smart-waste-management-system-market/request-sampleSmart Waste Management System Market is categorized based on geography into Asia Pacific (China, India, ASEAN, Australia & New Zealand), Japan, Middle East and Africa (GCC countries, S. Africa, Rest Of MEA), North America (U.S., Canada), Latin America (Brazil, Rest of Latin America), Western Europe (Germany, Italy, France, England, Spain, Rest of Western Europe), and Eastern Europe (Poland, Russia, Rest of Eastern Europe).Asia-Pacific has been at the forefront with regards to Smart Waste Management System Market and will continue to rule the roost in the years to come.Some of the key players that fuel the growth of the Smart Waste Management System Market include Veolia North America, BRE SMART Waste, Republic Services, Covanta Energy, Harvest Power and Recycle Smart Solutions. The key players are focusing on inorganic growth to sustain themselves amidst fierce competition. As such, mergers, acquisitions, and joint ventures are the need of the hour.View Full Report with TOC @ https://www.millioninsights.com/industry-reports/smart-waste-management-system-marketMarket Segment:This report studies the global Smart Waste Management System market, analyzes and researches the Smart Waste Management System development status and forecast in United States, EU, Japan, China, India and Southeast Asia. This report focuses on the top players in global market, like• IBM Corporation• SAP SE• Waste Management• Enevo Oy• BigBelly Solar• SmartBin• Ecube Labs• Urbiotica SL• Pepperl+FuchsMarket segment by Regions/Countries, this report covers• United States• EU• Japan• China• India• Southeast AsiaMarket segment by Type, the product can be split into• Asset Management• Analytics & Reporting• Fleet Tracking & Monitoring• Mobile Workforce ManagementMarket segment by Application, Smart Waste Management System can be split into• Food & Retail• Construction• Manufacturing & Industrial• Health Care• Municipalities• Colleges & UniversitiesKey questions answered in the report include1. What will be the market size in 2025?2. How will the market change over the forecast period and what will be the market size in 2025?3. What are the drivers and restraint associated with the market and how will these factors affect the dynamics over the forecast period?4. What are the growth areas within the market space and where should a participant focus to get maximum ROI?