Facebook is Making Deep Learning Experimentation Easier With These Two New PyTorch-Based Frameworks

Streamlining the cycle from experimentation to production is one of the hardest things to achieve in modern machine learning applications. Among the deep learning frameworks in the market, Facebook-incubated PyTorch has become a favorite of the data science community for its flexibility in order to rapidly model and run experiments. However, many of the challenges of experimentation in deep learning applications go beyond the capabilities of a specific framework. The ability of data scientists to evaluate different models or hyperparameter configurations is typically hindered by the expensive computation resources and time needed to run those experiments. A few days ago, Facebook open sourced two new tools targeted to streamline adaptive experimentation in PyTorch applications: •Ax: Is an accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments. •BoTorch: Built on PyTorch, is a flexible, modern library for Bayesian optimization, a probabilistic method for data-efficient global optimization. The goal of both tools is to lower the barrier to entry for PyTorch developers to conduct rapid experiments in order to find optimal models for a specific problem. Both, Ax and BoTorch, are based on probabilistic models which simplify the exploration of a given environment in a machine learning problem. However, the two frameworks target different dimension of the experimentation problem space.


Comprehensive Introduction to Turing Learning and GANs: Part 1

This three-part tutorial continues my series on deep generative models. This topic on Turing learning and GANs is a natural extension to the previous topic on variational autoencoders (found here). We will see that GANs are largely superior to variational autoencoders, but are notoriously difficult to work with.


Top Javascript Machine Learning libraries in 2019

A hand-picked list of the best libraries by machinelearn.js community.
1. Tensorflow.js
2. Brain.js
3. stdlib-js
4. machinelearn.js
5. Math.js
6. face-api.js
7. R-js
8. natural


Need for Feature Engineering in Machine Learning

Feature Selection/Extraction is one of the most important concepts in Machine learning which is a process of selecting a subset of relevant features/ attributes (such as a column in tabular data) that are most relevant for the modelling and business objective of the problem and ignoring the irrelevant features from the data set. Yes, feature selection is really important. Irrelevant or partially relevant features can negatively impact model performance. It also becomes important when the number of features is very large we need not need to use every feature at our disposal. Benefits of Feature Engineering on to your Dataset
1. Reduce Overfitting
2. Improves Accuracy
3. Reduce Training Time
Let’s get into Practice how can we apply various feature engineering techniques to our dataset when the features are large and we don’t know how to select relevant information out of the dataset.


Google Launches AI Platform – an End-to-End Platform to Build, Run, and Manage ML Projects

by Abhishek Kaushik Follow Google has recently launched AI Platform, an end-to-end platform for developers and data scientists to build, test, and deploy machine learning models, at the Google Cloud Next 2019 conference held in San Francisco between April 9-11, 2019. The platform, launched in beta, brings together a host of products and services, both existing and new, to help businesses solve complex challenges using AI in a way that is easier and collaborative.


Data Science vs. Decision Science

Data science has become a widely used term and a buzzword as well. It is a broad field representing a combination of multiple disciplines. However, there are adjacent areas that deserve proper attention and should not be confused with data science. One of them is decision science. Its importance should not be underestimated, so it is useful to know the actual differences and peculiarities of these two fields. Data science and decision science are related but still separate fields, so at some points, it might be hard to compare them directly. In general, data scientist is a specialist involved in finding insights from data after this data has been collected, processed, and structured by data engineer. Decision scientist considers data as a tool to make decisions and solve business problems. To demonstrate other differences, we decided to prepare an infographic which puts data science and decision science in contrast according to several criteria. Let’s dive right in.


Using Modules in R

When a code base grows we may think of using several files first and then source them. Functions, of course, are rightfully advocated to new R users, and are the essential building block. Packages are then, already, the next level of abstraction we have to offer. With the modules package I want to provide something in between: local namespace definitions without, or within R packages. We find this feature implemented in various ways and languages: classes, namespaces, functions, packages, and sometimes also modules. Python, Julia, F#, Scala, and Erlang (and more) are languages which use modules as a language construct. Some of them in parallel to classes and packages.


Understanding PyTorch with an example: a step-by-step tutorial

There are many many PyTorch tutorials around and its documentation is quite complete and extensive. So, why should you keep reading this step-by-step tutorial? Well, even though one can find information on pretty much anything PyTorch can do, I missed having a structured, incremental and from first principles approach to it. In this post, I will guide you through the main reasons why PyTorch makes it much easier and more intuitive to build a Deep Learning model in Python – autograd, dynamic computation graph, model classes and more – and I will also show you how to avoid some common pitfalls and errors along the way. Moreover, since this is quite a long post, I built a Table of Contents to make navigation easier, should you use it as a mini-course and work your way through the content one topic at a time.


How is Data Science Changing the World?

In this article, you will go through the role that a Data Scientist plays. There is a veil of mystery surrounding Data Science. While the buzzword of Data Science has been circulating for a while, very few people know about the real purpose of being a Data Scientist. So, let’s explore the purpose of Data Science.


https://towardsdatascience.com/putting-the-science-in-data-science-8dbd9bb83c72

Proper scientific research starts somewhere at a vaguely specified question or general wonder. What process governs some specific process? Why do we witness a certain effect? Or, how can we explain a behavior? These general wonders need to be formalized in conjectures or hypotheses which in turn can be tested. This forms the true starting point of scientific enquiry. The corporate Data Scientist, on the other hand, is too often ‘looking at what the data says.’ Their starting point is, conceptually, somewhere mid-point on a methodological framework. This is problematic for many reasons. There is no a priori knowledge that the data is representative for the problem at hand, that the sample size is statistically relevant, that the data can actually answer the question, or the system’s behavior ergodic. Most of all though, data is malleable; it is surprisingly easy for the data to follow a(ny) given narrative.


An Advanced Introduction to Artificial Intelligence

In artificial intelligence, the central problem at hand is that of the creation of a rational agent that has goals or preferences and tries to perform a series of actions that yield the optimal expected outcome given these goals. Rational agents exist in an environment, which is specific to the given instantiation of the agent. For example, the environment for a checkers agent is the virtual checkers board on which it plays against opponents, where piece moves are actions. Together, an environment and the agents that reside within it create a world. A reflex agent is one that doesn’t think about the consequences of its actions, but rather selects an action based solely on the current state of the world. These agents are typically outperformed by planning agents, which maintain a model of the world and use this model to simulate performing various actions. Then, the agent can determine hypothesized consequences of the actions and can select the best one. This is simulated ‘intelligence’ in the sense that it’s exactly what humans do when trying to determine the best possible move in any situation – thinking ahead.


Data Science in the Design Process

In recent years the digitisation of our lives and environments has created a surge in digital data. We leave behind digital traces of our behaviour both online and even offline, through the rise of wearable technology and the presence of our mobile phones. Technology has enabled the storage of this data by companies, who have started to realise the value that this data can have for their products, services and the marketing thereof. According to Harvard Business Review, the role of data scientists has become ‘the sexiest job of the 21st century’ (Davenport, 2012), and organisations from all industries are using data science to extract value from the large amounts of data (big data) they are collecting. The need to use data science has also reached service design agencies, as more and more clients put pressure on their design agencies to incorporate the use of data into their ways of working. This study therefore aims to address to what extent service designers include data science in the design process. The approach of Research through Design is used in order to investigate how designers have been using data science, as traditionally the design process is deeply rooted in qualitative research. The research uncovers the broad challenges faced by designers working with data and how data can be used not only to complement qualitative insights, but explores data as a new medium of design. Furthermore, a framework and toolkit are suggested as proposed solutions, and the report illustrates how the prototypes were developed, tested and iterated. Next steps in the development of the proposed solutions are highlighted and finally, recommendations are made for further areas of practice-based academic research in this field.


Seven steps to Machine Learning

These are the steps and what’s covered in the video:
1. Data collection: the data lake delusion.
2. Data curation: data schema, semantic types, missing value handling, data aggregation.
3. Data exploration: measures of tendency/dispersion, visualizations.
4. Feature Engineering: feature selection, dimensionality reduction, subject matter expert vs. data scientist synergy.
5. Modeling: how to select a Machine Learning algorithm? Supervised vs. unsupervised learning, recommendation systems, open source tools.
6. Evaluation: how to pick the best model?
7. Deployment: from a model to a service running on the cloud.


A Road Map for Deep Learning

Deep learning is a form of machine learning which allows a computer to learn from experience and understand things from a hierarchy of concepts where each concept being defined from a simpler one. This approach avoids the need for humans to specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them on top of each other through a deep setup with many layers.


Representing music with Word2vec?

Machine learning algorithms have transformed the field of vision and NLP. But what about music? These last few years, the field of music information retrieval (MIR) has been experiencing a rapid growth. We will be looking at how some of these techniques from NLP can be ported to the field of music. In a recent paper by Chuan, Agres, & Herremans (2018), they explore how a popular technique from NLP, namely word2vec, can be used to represent polyphonic music. Let’s dive into how this was done…


Transfer Learning : Picking the right pre-trained model for your problem

Transfer learning is the art of using pre-trained models to solve deep learning problems. A pre-trained model is nothing but a deep learning model someone else built and trained on some data to solve some problem. Transfer Learning is a machine learning technique where you use a pre-trained neural network to solve a problem that is similar to the problem the network was originally trained to solve. For example, you could re-purpose a deep learning model built to identify dog breeds to classify dogs and cats, instead of building your own. This could save you the pain of finding an effective neural network architecture, the time you spend on training, the trouble of building a large corpus of training data and guarantee good results. You could spend ages coming up with a fifty layered CNN for perfectly differentiating your cats from your dogs or you could simply re-purpose one of the many pre-trained image classification models available online. Now, Let us look at what exactly this re-purposing involve.
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