Since we originally proposed the need for a first-class language, compiler and ecosystem for machine learning (ML) - a view that is increasingly shared by many, there have been plenty of interesting developments in the field. Not only have the tradeoffs in existing systems, such as TensorFlow and PyTorch, not been resolved, but they are clearer than ever now that both frameworks contain distinct "static graph" and "eager execution" interfaces. Meanwhile, the idea of ML models fundamentally being differentiable algorithms – often called differentiable programming – has caught on.

Where current frameworks fall short, several exciting new projects have sprung up that dispense with graphs entirely, to bring differentiable programming to the mainstream. Myia, by the Theano team, differentiates and compiles a subset of Python to high-performance GPU code. Swift for TensorFlow extends Swift so that compatible functions can be compiled to TensorFlow graphs. And finally, the Flux ecosystem is extending Julia’s compiler with a number of ML-focused tools, including first-class gradients, just-in-time CUDA kernel compilation, automatic batching and support for new hardware such as TPUs.

This talk will demonstrate how Julia is increasingly becoming a natural language for machine learning, the kind of libraries and applications the Julia community is building, the contributions from India (there are many!), and our plans going forward.

It is too tedious to keep on asking questions, seek explanations or set thresholds for trends or anomalies. Why not find problems before they happen, find explanations for the glitches and suggest shortest paths to fixing them? Businesses are always changing along with their competitive environment and processes. No static model can handle that. Using dynamic models that find time-delayed interactions between multiple time series, we need to make proactive forecasts of anomalous trends of risks and opportunities in operations, sales, revenue and personnel, based on multiple factors influencing each other over time. We need to know how to set what is “normal” and determine when the business processes from six months ago do not apply any more, or only applies to 35% of the cases today, while explaining the causes of risk and sources of opportunity, their relative directions and magnitude, in the context of the decision-making and transactional applications, using state-of-the-art techniques.

Real world processes and businesses keeps changing, with one moving part changing another over time. Can we capture these changing relationships? Can we use multiple variables to find risks on key interesting ones? We will take a fun journey culminating in the most recent developments in the field. What methods work well and which break? What can we use in practice?

For instance, we can show a CEO that they would miss their revenue target by over 6% for the quarter, and tell us why i.e. in what ways has their business changed over the last year. Then we provide the prioritized ordered lists of quickest, cheapest and least risky paths to help turn them over the tide, with estimates of relative costs and expected probability of success.

The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

A machine learning or deep learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. There are some domains in the industry especially in the world of finance like insurance or banking where data scientists often end up having to use more traditional machine learning models (linear or tree-based). The reason being that model interpretability is very important for the business to explain each and every decision being taken by the model.However, this often leads to a sacrifice in performance. This is where complex models like ensembles and neural networks typically give us better and more accurate performance (since true relationships are rarely linear in nature).We, however, end up being unable to have proper interpretations for model decisions.

To address and talk about these gaps, I will take a conceptual yet hands-on approach where we will explore some of these challenges in-depth about explainable artificial intelligence (XAI) and human interpretable machine learning and even showcase with some examples using state-of-the-art model interpretation frameworks in Python!

With the big boom in Data Science and Analytics Industry in India, a lot of data scientists are keen on learning a variety of learning algorithms and data manipulation techniques. At the same time, there is this growing interest among data scientists to give back to the society, harness their acquired skills and help fix some of the major burning problems in the nation. But how does one go about finding meaningful datasets connecting to societal problems and plan data-for-good projects? This session will summarize our experience of working in Data-for-Good sector in last 5 years, sharing few interesting datasets and associated use-cases of employing machine learning and artificial intelligence in social sector. Indian social sector is replete with good volume of open data on attributes like annotated images, geospatial information, time-series, Indic languages, Satellite Imagery, etc. We will dive into understanding journey of a Data-for-Good project, getting essential open datasets and understand insights from certain data projects in development sector. Lastly, we will explore how we can work with various communities and scale our algorithmic experiments in meaningful contributions.

Machine learning and deep learning have been rapidly adopted in various spheres of medicine such as discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating biomedical data into improved human healthcare. Machine learning/deep learning based healthcare applications assist physicians to make faster, cheaper and more accurate diagnosis.

We have successfully developed three deep learning based healthcare applications and are currently working on two more healthcare related projects. In this workshop, we will discuss one healthcare application titled "Deep Learning based Craniofacial Distance Measurement for Facial Reconstructive Surgery" which is developed by us using TensorFlow. Craniofacial distances play important role in providing information related to facial structure. They include measurements of head and face which are to be measured from image. They are used in facial reconstructive surgeries such as cephalometry, treatment planning of various malocclusions, craniofacial anomalies, facial contouring, facial rejuvenation and different forehead surgeries in which reliable and accurate data are very important and cannot be compromised.

Our discussion on healthcare application will include precise problem statement, the major steps involved in the solution (deep learning based face detection & facial landmarking and craniofacial distance measurement), data set, experimental analysis and challenges faced & overcame to achieve this success. Subsequently, we will provide hands-on exposure to implement this healthcare solution using TensorFlow. Finally, we will briefly discuss the possible extensions of our work and the future scope of research in healthcare sector.

Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.

The advent of machine learning along with its integration with computer vision has enabled users to efficiently to develop image-based solutions for innumerable use cases. A machine learning model consists of an algorithm which draws some meaningful correlation between the data without being tightly coupled to a specific set of rules. It's crucial to explain the subtle nuances of the network along with the use-case we are trying to solve. With the advent of technology, the quality of the images has increased which in turn has increased the need for resources to process the images for building a model. The main question, however, is to discuss the need to develop lightweight models keeping the performance of the system intact.
To connect the dots, we will talk about the development of these applications specifically aimed to provide equally accurate results without using much of the resources. This is achieved by using image processing techniques along with optimizing the network architecture.
These applications will range from recognizing digits, alphabets which the user can 'draw' at runtime; developing state of the art facial recognition system; predicting hand emojis, developing a self-driving system, detecting Malaria and brain tumor, along with Google's project of 'Quick, Draw' of hand doodles.
In this presentation, we will discuss the development of such applications with minimization of CPU usage.

Cleaning, preparing , transforming, exploring data and modeling it's what we hear all the time about data science, and these steps maybe the most important ones. But that's not the only thing about data science, in this talk you will learn how the combination of Apache Spark, Optimus, the Python ecosystem and Data Operations can form a whole framework for data science that will allow you and your company to go further, and beyond common sense and intuition to solve complex business problems.

This will be a hands-on workshop how to build a custom interactive dashboard application on your local machine or on any cloud service provider. You will also learn how to deploy this application with both security and scalability in mind.

Powerful Data visualization software solutions are extremely useful when building interactive data visualization dashboards. However, these types of solutions might not provide sufficient customization options. For those scenarios, you can use open source libraries like D3.js, Chart.js, or Bokeh to create custom dashboards. While these libraries offer a lot of flexibility for building dashboards with tailored features and visualizations.

Banks and financial institutes in India over the last few years have increasingly faced defaults by corporates. In fact, NBFC stocks have suffered huge losses in recent times. It has triggered a contagion which spilled over to other financial stocks too and adversely affected benchmark indices resulting in short term bearishness. This makes it imperative to investigate ways to prevent rather than cure such situations. However, the banks face a twin-faced challenge in terms of identifying the probable wilful defaulters from the rest and moral hazard among the bank employees who are many a time found to be acting on behest of promoters of defaulting firms. The first challenge is aggravated by the fact that due diligence of firms before the extension of loan is a time-consuming process and the second challenge hints at the need for placement of automated safeguards to reduce mal-practises originating out of the human behaviour. To address these challenges, the automation of loan sanctioning process is a possible solution. Hence, we identified important firmographic variables viz. financial ratios and their historic patterns by looking at the firms listed as dirty dozen by Reserve Bank of India. Next, we used k-means clustering to segment these firms and label them into various categories viz. normal, distressed defaulter and wilful defaulter. Besides, we utilized text and sentiment analysis to analyze the annual reports of all BSE and NSE listed firms over the last 10 years. From this, we identified word tags which resonate well with the occurrence of default and are indicators of financial performance of these firms. A rigorous analysis of these word tags (anagrams, bi-grams and co-located words) over a period of 10 years for more than 100 firms indicate the existence of a relation between frequency of word tags and firm default. Lift estimation of firmographic financial ratios namely Altman Z score and frequency of word tags for the first time uncovers the importance of text analysis in predicting financial performance of firms and their default. Our investigation also reveals the possibility of using neural networks as a predictor of firm default. Interestingly, the neural network developed by us utilizes the power of open source machine learning libraries and throws open possibilities of deploying such a neural network model by banks with a small one-time investment. In short, our work demonstrates the ability of machine learning in addressing challenges related to prevention of wilful default. We envisage that the implementation of neural network based prediction models and text analysis of firm-specific financial reports could help financial industry save millions in recovery and restructuring of loans.

Introduction:

Synthesizing audio for specific domains has many practical applications in creative sound design for music and film. But the application is not restricted to entertainment industry. We propose an architecture that will convert audio (human voice) to the voice owner’s preferred image – for the time being we restrict the intended images to two domains – Object Design and Human body. Many times, human beings are unable to describe a design (may be power-point presentation or interior decoration of a house) or a known person by verbally described attributes as they are able to visualise the same design or the person. But the other person, the listener may be unable to interpret the object or human descriptions from the speaker’s verbal descriptions as he/she is not visualising the same. Complete communication thus needs much of a trial and error and overall hazardous and time consuming. Examples of such situations are 1) While making presentation, an executive or manager can visualise something and an express to his/her employee to make the same. But, making the best slides from manger’s description may not be proper. Another relevant example is that a house owner or office owner wants his/her premises to have certain design which he/she can visualise and express to the concerned vendor. But the vendor may not be able to produce the same. Also, trial and error in this case is highly expensive. Having an automated Image, recommended to him/her can address this problem. 2) Verbal description of a terrorist or criminal suspect (facial description and/or attribute) may not be always available to all the security people every time, in Airports or Railway Stations or sensitive areas. Presence of a software system having Machine Generated Image with Ranked Recommendation for such suspect can immediately point to one or very few people in a crowded Airport or even Railway Station or any such sensitive place. Security agencies can then frisk only those people or match their attributes with existing database. This can avoid hazardous manual checking of every people in the same process and can help the security agencies to do adequate checking for those recommended individuals.

We can use a Sequential Architecture consisting of simple NLP and more complex Deep Learning algorithms primarily based on Generative Adversarial Network (GAN) and Neural Personalised Ranking (NPR) to help the object designers and security personnel for serving their specific purposes.

The idea to combat the problem:

I propose a combination of Deep Learning and Recommender System approach to tackle this problem. Architecture of the Solution model consists of 4 major Components – 1) Speech to Text

2) Text Classification into Person or Design; 3) Text to Image Formation; 4) Recommender System

We are trying to address these four steps in consecutive applications of effective Machine Learning and Deep Learning Algorithms. Deep Learning community has already been able to make significant progress in terms of Text to Image generation and also in Ranking based Recommender System

Brief Details about the four major pillars of this problem:

Deep Learning based Speech Recognition – Primary technique for Speech to text could be Baidu’s DeepSpeech for which a Tensorflow implementation is readily available. Also, Google Cloud Speech-to-Text enables the develop to convert Voice to text. Voice of the user needs to be converted in .wav file. Our steps for Deep-Speech-2 are like this – Fixing GPU memory, Adding Batch normalization to RNN, implement row Convolution layer and generate text.

Nowadays, we have quite a few free Speech to Text software, e.g. Google Docs Voice typing, windows Speech Recognition, Speech-notes etc.

Text Classification of Content – This is needed to classify the converted text into two classes – a) Design Description or b) Human Attribute Description because these two applications and therefore image types are different. This may be Statistically easier part, but its importance is immense. A Dictionary of words related to Designs and Personal Attributes can be built using online available resources. Then, a supervised algorithm using tf-idf and Latent Semantic Analysis (LSA) should be able to classify the text into two classes – Object and Person. These are very much traditional and proven techniques in many NLP research

Text to Image Formation – This is our main component for this proposal. Today, one of the most challenging problems in the world of Computer Vision is synthesizing high-quality images from text descriptions. In recent years, GANs have been found to generate good results. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. There have been a few approaches to address this problem, all using GAN. One of those is given as Stacked Generative Adversarial Networks (StackGAN). Heart of such approaches is Conditional GAN which is an extension of GAN where both generator and discriminator receive additional conditioning variables c, yielding G(z, c) and D(x, c). This formulation allows G to generate images conditioned on variables c.

In our case, we train deep convolutional generative adversarial network (DC-GAN) conditioned on text features. These text features are encoded by a hybrid character-level convolutional-recurrent neural network. Overall, DC-GAN uses text embeddings where the context of a word is of prime importance. Class label determined in the earlier step will be of help in this case. This will simply help DC-GAN to generate more relevant images than irrelevant ones. Details will be discussed during the talk

The most straightforward way to train a conditional GAN is to view (text, image) pairs as joint observations and train the discriminator to judge pairs as real or fake. The discriminator has no explicit notion of whether real training images match the text embedding context. To account for this, in GAN-CLS, in addition to the real/fake inputs to the discriminator during training, a third type of input consisting of real images with mismatched text is added, which the discriminator must learn to score as fake. By learning to optimize image/text matching in addition to the image realism, the discriminator can provide an additional signal to the generator. (details are in talk)

Image Recommender System – In the last step, we propose personalised image recommendation for the user from the set of images generated by GAN-CLS architecture. Image Recommendation brings down the number of choice of images to a top N (N=3, 5, 10 ideally) with a rank given to each of those and therefore user finds it easier to choose. In this case, we propose Neural Personalized Ranking (NPR) – a personalized pairwise ranking model over implicit feedback datasets – that is inspired by Bayesian Personalized Ranking (BPR) and recent advances in neural networks. We like to mention that, now NPR is improved to contextual enhanced NPR. This enhanced Model depends on implicit feedbacks from the users, its contexts and incorporates the idea of generalized matrix factorization. Contextual NPR significantly outperforms its competitors

In the presentation, we shall describe the complete sequence in detail

Recent advancements in AI are proving beneficial in development of applications in various spheres of healthcare sector such as microbiological analysis, discovery of drug, disease diagnosis, Genomics, medical imaging and bioinformatics for translating a large-scale data into improved human healthcare. Automation in healthcare using machine learning/deep learning assists physicians to make faster, cheaper and more accurate diagnoses.

Due to increasing availability of electronic healthcare data (structured as well as unstructured data) and rapid progress of analytics techniques, a lot of research is being carried out in this area. Popular AI techniques include machine learning/deep learning for structured data and natural language processing for unstructured data. Guided by relevant clinical questions, powerful deep learning techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making.

We have successfully developed three deep learning based healthcare applications using TensorFlow and are currently working on three more healthcare related projects. In this demonstration session, first we shall briefly discuss the significance of deep learning for healthcare solutions. Next, we will demonstrate two deep learning based healthcare applications developed by us. The discussion of each application will include precise problem statement, proposed solution, data collected & used, experimental analysis and challenges encountered & overcame to achieve this success. Finally, we will briefly discuss the other applications on which we are currently working and the future scope of research in this area.

This case study, followed by a demo, will parade on how handwritten date and amount fields were extracted and validated. By adopting this automated way of recognising handwritten digits, banks can cut down the manual time and increase speed in their process. Although this is still in the proof of concept phase, this feat was achieved using computer vision and image processing techniques.

This case study will briefly cover:

• Detection of bounding and taking the region of interest
• Fragment and Identify technique
• Checking the accuracy of bounding box using Intersection over Union technique

This case study/approach can be extended to other operative environments, where handwritten digits recognition is needed.

Containers are all the rage in the DevOps arena.

This session is a live demonstration of how the data team at Milliman uses containers at each step in their data science workflow -

1) How do containerized environments speed up data scientists at the data exploration stage

2) How do containers enable rapid prototyping and validation at the modeling stage

3) How do we put containerized models on production

4) How do containers make it easy for data scientists to do DevOps

5) How do containers make it easy for data scientists to host a data science dashboard with continuous integration and continuous delivery

Open Data offers a tremendous opportunity in transformation of today’s manufacturing sector to smarter manufacturing. Smart Manufacturing initiatives include digitalising production processes and integrating IoT technologies for connecting machines to collect data for analysis and visualisation.

In this talk, an understanding of linkage between various industries within manufacturing sector through lens of Open Data Science will be illustrated. The data on manufacturing sector companies, company profiles, officers and financials will be scraped from UK Open Data API’s. The work I plan to showcase in ODSC is part of UK Made Smarter Project, where the work has been useful for major aerospace alliances to find out the champions and strugglers (SMEs) within manufacturing sector based on the open data gathered from multiple sources. The talk includes discussion on data extraction, data cleaning, data transformation - transforming raw financial information about companies to key metrics of interest - and further data analytics to create clusters of manufacturing companies into "Champions" and "Strugglers". The talk showcased examples of powerful R Shiny based dashboards of interest for suppliers, manufacturer and other key stakeholders in supply chain network.

Further analysis includes network analysis for industries, clustering and deploying the model as an API using Google Cloud Platform. The presenter will discuss about the necessity of 'Analytical Thinking' approach as an aid to handle complex big data projects and how to overcome challenges while working with real-life data science projects.

Computer Vision has lots of applications including medical imaging, autonomous
vehicles, industrial inspection and augmented reality. Use of Deep Learning for
computer Vision can be categorized into multiple categories for both images and
videos – Classification, detection, segmentation & generation.
Having worked in Deep Learning with a focus on Computer Vision have come
across various challenges and learned best practices over a period
experimenting with cutting edge ideas. This workshop is for Data Scientists &
Computer Vision Engineers whose focus is deep learning. We will cover state of
the art architectures for Image Classification, Segmentation and practical tips &
tricks to train a deep neural network models. It will be hands on session where
every concepts will be introduced through python code and our choice of deep
learning framework will be PyTorch v1.0 and Keras.

Given we have only 8 hours, we will cover the most important fundamentals,
current techniques and avoid anything which is obsolete or not being used by
state-of-art algorithms. We will directly start with building the intuition for
Convolutional Neural Networks, and focus on core architectural problems. We
will try and answer some of the hard questions like how many layers must be
there in a network, how many kernels should we add. We will look at the
architectural journey of some of the best papers and discover what each brought
into the field of Vision AI, making today’s best networks possible. We will cover 9
different kinds of Convolutions which will cover a spectrum of problems like
running DNNs on constrained hardware, super-resolution, image segmentation,
etc. The concepts would be good enough for all of us to move to harder problems
like segmentation or super-resolution later, but we will focus on object
recognition, followed by object detections. We will build our networks step by
step, learning how optimizations techniques actually improve our networks and
exactly when should we introduce them. We hope the leave you in confidence
which will help you read research papers like your second nature. Given we have
8 hours, and we want the sessions to be productive, we will instead of introducing

all the problems and solutions, focus on the fundamentals of modern deep neural
networks.

“We have built a machine learning model, What next?”

There is quite a bit of journey that one needs to cover from building a model in Jupyter notebook to taking it to production.
I would like to call it as the “last mile problem in ML” , this last mile could be a simple tread if we embrace some good ideas.

This talk covers some of these opinionated ideas on how we can get around some of the pitfalls in deployment of ML models in production.

We would go over the below questions in detail think about solutions for them.

• How to fix the zombie models apocalypse, a state when nobody knows how the model was trained ?
• In Science, experiments are found to be valid only if they are reproducible. Should this be the case in Datascience as well ?
• Training the model in your local machine and waiting for an eternity to complete is no fun. What are some better ways of doing this ?
• How do you package your machine learning code in a robust manner?
• Does an ML project have the luxury of not following good Software Engineering principles?

Since 2014, adversarial examples in Deep Neural Networks have come a long way. This talk aims to be a comprehensive introduction to adversarial attacks including various threat models (black box/white box), approaches to create adversarial examples and will include demos. The talk will dive deep into the intuition behind why adversarial examples exhibit the properties they do — in particular, transferability across models and training data, as well as high confidence of incorrect labels. Finally, we will go over various approaches to mitigate these attacks (Adversarial Training, Defensive Distillation, Gradient Masking, etc.) and discuss what seems to have worked best over the past year.

Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

We will evaluate whether stock returns can be predicted based on historical information.

Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value

India is a land of many languages. There are 23 official and much more unofficial languages prevalently used in day-to-day conversations. Unfortunately, information dissemination to the low resource languages get difficult because of the geo-spatial distances. Popular translation platforms helped to fill this gap in major languages but their efficiency is challenged by the lack of availability of proper datasets and their generic nature. This problem is very evident when more domain information gets involved.

We present Samantar, an open translation suggestion framework targeted at Indian languages. Samantar is built with open parallel corpora and opensource technologies. The translations can be tuned to suggest according to different target domains.