Lecture 10 introduces translation, machine translation, and neural machine translation. Google’s new NMT is highlighted followed by sequence models with attention as well as sequence model decoders.

——————————————————————————-

Natural Language Processing with Deep Learning

Instructors:
– Chris Manning
– Richard Socher

Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component.

For additional learning opportunities please visit:
http://stanfordonline.stanford.edu/

So you’ve built / found the perfect Deep Learning model, now how do you put that into production and maintain it? This talk is all about Machine Learning Software Engineering (MLEng) on the Cloud using Java technologies. In particular we will cover :

– Machine Learning Quick Primer (Code & Concepts) using TensorFlow for Java, Tribuo, DJL and PyTorch for Java

– Use Case Deep Dive : Learn how to take pre-trained ML model and deploy it using micro-services architecture (Micronaut framework) on the Cloud

The talk will be hands on with a mix of presentation and live coding. All the code will be available on GitHub after the presentation.

Rick van de Zedde & Pieter de Visser, Wageningen University & Research.

At Wageningen University & Research from April 2021 onwards, a digital twin (DT) will be operational. The DT will digitally represent a tomato crop of individual, virtual plants in their local greenhouse environment, and grown simultaneously. The DT will feature real-time updating of plant parameters and environmental variables based on high-tech sensor equipment available in the Netherlands Plant Eco-phenotyping Centre (NPEC) facilities. In the DT, each tomato plant in the crop will be modelled in 3D integrating a set of traits that correspond to model parameters. Thereby, the DT enables us to predict crop response (growth, development and production) to greenhouse and management conditions that affect production efficiency; light intensity and quality, CO2 dosing, nutrient availability and leaf pruning. Thus, the DT can support greenhouse management in real-time. This will be the first-ever 3D simulation model of individual plants growing in greenhouses that get updated by sensor data and that delivers updated predictions as the real plants grow. In that sense, it is a true digital twin, which does not yet exist for plants. This is an important extension of the plant and greenhouse modelling that exists today. As well, the DT allows for hypothesis testing and in silico experiments. As a scientific aim, we will develop and study novel methods on e.g. deep learning for processing of sensor data to transform the raw data to plant traits. Moreover, novel methods will be dealt with on Bayesian inference of state parameters of the plant and greenhouse models, allowing efficient model updating and optimizing the accuracy of the model predictions.
Scientific issues that will be addressed include processing the high-dimensional sensor data, further refinement of the plant and greenhouse model, estimating the model parameters and using that to make a decision about control. Furthermore, with our systematic and process-based approach we can analyse the whole system, and investigate possible bottlenecks in sensing, modelling, and control, and in what way or to which extent they hinder optimal performance. For example by simulating how small errors in each of the modules propagate through the system and influence the performance. The subsequent investigation can then be targeted efficiently to find remedies, e.g., by improved sensing equipment or algorithms, improved model accuracy, or by selecting a different type of controller. The constructed DT can be used to predict the growth and development of tomato plants in response to real-time environmental factors and management decisions. This allows for more informed decisions regarding agronomic management in commercial practice, as well as the selection pressure applied by breeders to specific traits.
More info: https://www.npec.nl/news/wur-is-working-on-digital-twins-for-tomatoes-food-and-farming/

Etincelle #15 – March 31, 2021

Rick van de Zedde – Pieter de Visser
Wageningen University & Research

Rick van de Zedde is project manager of the new phenotyping facility NPEC @ WUR, next to that he is senior scientist/ business developer Phenomics and Automation at the Wageningen Plant Science Group where he has worked at WUR since 2004. His background is in Artificial Intelligence with a focus on imaging and robotics. Netherlands Plant Eco-phenotyping Centre (NPEC) is an integrated, national research facility housed by Wageningen University & Research and Utrecht University and is co-funded by The Netherlands Organisation for Scientific Research (NWO). More info www.npec.nl. Pieter de Visser is a senior scientist in the team Crop Physiology of the business unit Greenhouse Horticulture at the Wageningen Plant Science Group. Since 2001 he developed into an expert in novel crop simulation models, in particular 3D crop models on architecture and physiology and self-learning models that are linked to plant sensors. The models are applied in decision support systems for horticulture with the focus on crop production, energy use and climate-related crop diseases.

TechBites: Digital twin – älyä suunnitteluun
22 May 2019, Tampere University, Hervanta Campus
http://techbites.fi/

Professorit Asko Ellman ja Kari Koskinen tutkivat ja kehittävät Tampereen yliopiston tekniikan ja luonnontieteiden tiedekunnassa suunnittelumenetelmiä ja elinkaaren hallintamenetelmiä. Suunnittelutyökalut, kuten XR, SXR, Digital Twin, AI ja ML tuovat uusia mahdollisuuksia koneiden, tuotannon ja rakennetun ympäristön elinkaaren hallintaan. Näissä on nähtävissä isoja mahdollisuuksia suunnittelun tuottavuuden kasvattamiseen.

Digital twins are invaluable when it comes to optimizing processes in the product life cycle of machine tools and implementing business models. https://www.siemens.com/sinumerik-digitaltwin

#aifoundation #aijourney #artificialintelligence

Hi, everyone. You are very welcome to week two of our NLP course. And this week is about very core NLP tasks. So we are going to speak about language models first, and then about some models that work with sequences of words, for example, part-of-speech tagging or named-entity recognition. All those tasks are building blocks for NLP applications. And they’re very, very useful. So first thing’s first. Let’s start with language models. Imagine you see some beginning of a sentence, like This is the. How would you continue it? Probably, as a human,you know that This is how sounds nice, or This is did sounds not nice. You have some intuition. So how do you know this? Well, you have written books. You have seen some texts. So that’s obvious for you. Can I build similar intuition for computers? Well, we can try. So we can try to estimate probabilities of the next words, given the previous words. But to do this, first of all,we need some data. So let us get some toy corpus. This is a nice toy corpus about the house that Jack built. And let us try to use it to estimate the probability of house, given This is the. So there are four interesting fragments here. And only one of them is exactly what we need. This is the house. So it means that the probability will be one 1 of 4. By c here, I denote the count. So this the count of This is the house,or any other pieces of text. And these pieces of text are n-grams. n-gram is a sequence of n words. So we can speak about 4-grams here. We can also speak about unigrams, bigrams, trigrams, etc. And we can try to choose the best n,and we will speak about it later. But for now, what about bigrams? Can you imagine what happens for bigrams, for example, how to estimate probability of Jack,given built? Okay, so we can count all different bigrams here, like that Jack, that lay, etc., and say that only four of them are that Jack. It means that the probability should be 4 divided by 10. So what’s next? We can count some probabilities. We can estimate them from data. Well, why do we need this? How can we use this? Actually, we need this everywhere. So to begin with,let’s discuss this Smart Reply technology. This is a technology by Google. You can get some email, and it tries to suggest some automatic reply. So for example, it can suggest that you should say thank you. How does this happen? Well, this is some text generation, right? This is some language model. And we will speak about this later,in many, many details, during week four. So also, there are some other applications, like machine translation or speech recognition. In all of these applications, you try to generate some text from some other data. It means that you want to evaluate probabilities of text, probabilities of long sequences. Like here, can we evaluate the probability of This is the house, or the probability of a long,long sequence of 100 words? Well, it can be complicated because maybe the whole sequence never occurs in the data. So we can count something, but we need somehow to deal with small pieces of this sequence, right? So let’s do some math to understand how to deal with small pieces of this sequence. So here, this is our sequence of keywords. And we would like to estimate this probability. And we can apply chain rule,which means that we take the probability of the first word, and then condition the next word on this word, and so on. So that’s already better. But what about this last term here? It’s still kind of complicated because the prefix, the condition, there is too long. So can we get rid of it? Yes, we can. So actually, Markov assumption says you shouldn’t care about all the history. You should just forget it. You should just take the last n terms and condition on them, or to be correct, last n-1 terms. So this is where they introduce assumption, because not everything in the text is connected. And this is definitely very helpful for us because now we have some chance to estimate these probabilities. So here, what happens for n = 2, for bigram model? You can recognize that we already know how to estimate all those small probabilities in the right-hand side,which means we can solve our task. So for a toy corpus again,we can estimate the probabilities. And that’s what we get. Is it clear for now? I hope it is. But I want you to think about if everything is nice here. Are we done?

ML Systems Workshop @ NIPS 2017
https://nips.cc/Conferences/2017/Schedule?showEvent=8774

Contributed Talk 3: NSML: A Machine Learning Platform That Enables You to Focus on Your Models by Nako Sung. This Video is by Jung-Woo Ha.

Deployment Videos Link :https://www.youtube.com/watch?v=bjsJOl8gz5k&list=PLZoTAELRMXVOAvUbePX1lTdxQR8EY35Z1

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Data scientists spend a lot of time on data cleaning and munging, so that they can finally start with the fun part of their job: building models. After you have engineered the features and tested different models, you see how the prediction performance improves. However, the job is not done when you have a high performing model. The deployment of your models is a crucial step in the overall workflow and it is the point in time when your models actually become useful to your company.

In this session you will learn about various possibilities and best practices to bring machine learning models into production environments. The goal is not only to make live prediction calls or have the models available as REST API, but also what needs to be considered to maintain them. This talk will focus on solutions with Python (flask, Cloud Foundry, Docker, and more) and the well established ML packages such as Spark MLlib, scikit-learn, and xgboost, but the concepts can be easily transferred to other languages and frameworks.

Speaker
SUMIT GOYAL
Software Engineer
IBM

To learn more, please visit: https://aws.amazon.com/sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this tech talk, we will introduce you to the concepts of Amazon SageMaker including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment of ML models. With zero setup required, Amazon SageMaker significantly decreases your training time and the overall cost of getting ML models from concept to production.

Learning Objectives:
– Learn the fundamentals of building, training & deploying machine learning models
– Learn how Amazon SageMaker provides managed distributed training for machine learning models with a modular architecture
– Learn to quickly and easily build, train & deploy machine learning models using Amazon SageMaker

In this video, we will talk about first text classification model on top of features that we have described.

And let’s continue with the sentiment classification. We can actually take the IMDB movie reviews dataset, that you can download, it is freely available. It contains 25,000 positive and 25,000 negative reviews. And how did that dataset appear? You can actually look at IMDB website and you can see that people write reviews there, and they actually also provide the number of stars from one star to ten star. They actually rate the movie and write the review. And if you take all those reviews from IMDB website, you can actually use that as a dataset for text classification because you have a text and you have a number of stars, and you can actually think of stars as sentiment. If we have at least seven stars, you can label it as positive sentiment. If it has at most four stars, that means that is a bad movie for a particular person and that is a negative sentiment. And that’s how you get the dataset for sentiment classification for free. It contains at most 30 reviews per movie just to make it less biased for any particular movie.

These dataset also provides a 50/50 train test split so that future researchers can use the same split and reproduce their results and enhance the model. For evaluation, you can use accuracy and that actually happens because we have the same number of positive and negative reviews. So our dataset is balanced in terms of the size of the classes so we can evaluate accuracy here.

Okay, so let’s start with first model. Let’s takes features, let’s take bag 1-grams with TF-IDF values. And in the result, we will have a matrix of features, 25,000 rows and 75,000 columns, and that is a pretty huge feature matrix. And what is more, it is extremely sparse. If you look at how many 0s are there, then you will see that 99.8% of all values in that matrix are 0s. So that actually applies some restrictions on the models that we can use on top of these features.

And the model that is usable for these features is logistic regression, which works like the following. It tries to predict the probability of a review being a positive one given the features that we gave that model for that particular review. And the features that we use, let me remind you, is the vector of TF-IDF values. And what you actually can do is you can find the weight for every feature of that bag of force representation. You can multiply each value, each TF-IDF value by that weight, sum all of that things and pass it through a sigmoid activation function and that’s how you get logistic regression model.

And it’s actually a linear classification model and what’s good about that is since it’s linear, it can handle sparse data. It’s really fast to train and what’s more, the weights that we get after the training can be interpreted.

And let’s look at that sigmoid graph at the bottom of the slide. If you have a linear combination that is close to 0, that means that sigmoid will output 0.5. So the probability of a review being positive is 0.5. So we really don’t know whether it’s positive or negative. But if that linear combination in the argument of our sigmoid function starts to become more and more positive, so it goes further away from zero. Then you see that the probability of a review being positive actually grows really fast. And that means that if we get the weight of our features that are positive, then those weights will likely correspond to the words that a positive. And if you take negative weights, they will correspond to the words that are negative like disgusting or awful.

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In Lecture 13 we move beyond supervised learning, and discuss generative modeling as a form of unsupervised learning. We cover the autoregressive PixelRNN and PixelCNN models, traditional and variational autoencoders (VAEs), and generative adversarial networks (GANs).

Keywords: Generative models, PixelRNN, PixelCNN, autoencoder, variational autoencoder, VAE, generative adversarial network, GAN

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture13.pdf

————————————————————————————–

Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Hello All,
In this video we will be discussing about the differences Between Infrastructure as a Service and Platform as a Service cloud platforms

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Let’s discuss whether you should train your models locally or in the cloud. I’ll go through several dedicated GPU options, then compare three cloud options; AWS, Google Cloud, and FloydHub. I was not endorsed by anyone for this.

Code for this video:
https://github.com/floydhub/fast-style-transfer

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High Budget GPU: Titan XP https://www.amazon.com/NVIDIA-GeForce-Pascal-GDDR5X-900-1G611-2500-000/dp/B01JLKP3IS

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Small Budget GPU: https://www.amazon.com/dp/B01MF7EQJZ

Build a Deep Learning machine:
https://medium.com/@ncondo/build-a-deep-learning-rig-for-800-4434e21a424f
https://medium.com/towards-data-science/building-your-own-deep-learning-box-47b918aea1eb
https://www.oreilly.com/learning/build-a-super-fast-deep-learning-machine-for-under-1000

More learning resources:
http://www.infoworld.com/article/3179785/cloud-computing/aws-vs-azure-vs-google-cloud-which-free-tier-is-best.html
https://thehftguy.com/2016/06/15/gce-vs-aws-in-2016-why-you-should-never-use-amazon/
https://medium.com/@davidmytton/aws-vs-google-cloud-flexibility-vs-operational-simplicity-dca4324b03d4
https://news.ycombinator.com/item?id=13659914

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Watch this presentation to learn how to effectively build and deploy TensorFlow based Deep learning models on the mobile platforms.

Sample code: https://github.com/AndreaPisoni

EVENT:

TensorFlow and Deep Learning Singapore 2017

SPEAKER:

Andrea Pisoni

PERMISSIONS:

The original video was published on Engineers.SG YouTube channel with the Creative Commons Attribution license (reuse allowed).