Building a network is critical to your career growth as you become more and more experienced. And you don’t necessarily need to be an extrovert to grow your network day-in and day-out. But first, you should start by asking yourself a few questions:
This question is crucial as social capital works the same as any other relationship, you can’t just ask ask ask, you have to be able to contribute some sort of value to the relationship. The thing that most people often overthink is that it needs to be of high value or relevant to your industry? But, like most people you’re a multi-dimensional person, and you have interests that span beyond your 9–5 pm. So here’s an exercise for you to try:
Write down 3 things that you’ve learned about in the last year. It can be a book you read, a podcast you listened to, or maybe it’s a new app you’ve tried and have thoughts on.
When at a loss for what to offer someone you’re looking to network with simply offer any of the three things you’ve learned about. You’d be surprised at how receptive people are to this in exchange. Another way to add value for most people is to offer your feedback on their app/website or project that they’re working on. Make sure that you’re always offering something even if it feels like you don’t have to, this ensures that you have balanced relationships across your network.
#careers #networking #management #leadership #coaching #neural networks
TL;DR This is the first in a [series of posts] where I will discuss the evolution and future trends in the field of deep learning on graphs.
Deep learning on graphs, also known as Geometric deep learning (GDL) , Graph representation learning (GRL), or relational inductive biases , has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade  if not two , it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the ML community and even to the popular science press (with Quanta Magazine running a series of excellent articles on geometric deep learning for the study of manifolds, drug discovery, and protein science).
Graphs are powerful mathematical abstractions that can describe complex systems of relations and interactions in fields ranging from biology and high-energy physics to social science and economics. Since the amount of graph-structured data produced in some of these fields nowadays is enormous (prominent examples being social networks like Twitter and Facebook), it is very tempting to try to apply deep learning techniques that have been remarkably successful in other data-rich settings.
There are multiple flavours to graph learning problems that are largely application-dependent. One dichotomy is between node-wise and graph-wise problems, where in the former one tries to predict properties of individual nodes in the graph (e.g. identify malicious users in a social network), while in the latter one tries to make a prediction about the entire graph (e.g. predict solubility of a molecule). Furthermore, like in traditional ML problems, we can distinguish between supervised and unsupervised (or self-supervised) settings, as well as transductive and inductive problems.
Similarly to convolutional neural networks used in image analysis and computer vision, the key to efficient learning on graphs is designing local operations with shared weights that do message passing  between every node and its neighbours. A major difference compared to classical deep neural networks dealing with grid-structured data is that on graphs such operations are permutation-invariant, i.e. independent of the order of neighbour nodes, as there is usually no canonical way of ordering them.
Despite their promise and a series of success stories of graph representation learning (among which I can selfishly list the [acquisition by Twitter] of the graph-based fake news detection startup Fabula AI I have founded together with my students), we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision. In the following, I will try to outline my views on the possible reasons and how the field could progress in the next few years.
**Standardised benchmarks **like ImageNet were surely one of the key success factors of deep learning in computer vision, with some  even arguing that data was more important than algorithms for the deep learning revolution. We have nothing similar to ImageNet in scale and complexity in the graph learning community yet. The [Open Graph Benchmark] launched in 2019 is perhaps the first attempt toward this goal trying to introduce challenging graph learning tasks on interesting real-world graph-structured datasets. One of the hurdles is that tech companies producing diverse and rich graphs from their users’ activity are reluctant to share these data due to concerns over privacy laws such as GDPR. A notable exception is Twitter that made a dataset of 160 million tweets with corresponding user engagement graphs available to the research community under certain privacy-preserving restrictions as part of the [RecSys Challenge]. I hope that many companies will follow suit in the future.
**Software libraries **available in the public domain played a paramount role in “democratising” deep learning and making it a popular tool. If until recently, graph learning implementations were primarily a collection of poorly written and scarcely tested code, nowadays there are libraries such as [PyTorch Geometric] or [Deep Graph Library (DGL)] that are professionally written and maintained with the help of industry sponsorship. It is not uncommon to see an implementation of a new graph deep learning architecture weeks after it appears on arxiv.
Scalability is one of the key factors limiting industrial applications that often need to deal with very large graphs (think of Twitter social network with hundreds of millions of nodes and billions of edges) and low latency constraints. The academic research community has until recently almost ignored this aspect, with many models described in the literature completely inadequate for large-scale settings. Furthermore, graphics hardware (GPU), whose happy marriage with classical deep learning architectures was one of the primary forces driving their mutual success, is not necessarily the best fit for graph-structured data. In the long run, we might need specialised hardware for graphs .
**Dynamic graphs **are another aspect that is scarcely addressed in the literature. While graphs are a common way of modelling complex systems, such an abstraction is often too simplistic as real-world systems are dynamic and evolve in time. Sometimes it is the temporal behaviour that provides crucial insights about the system. Despite some recent progress, designing graph neural network models capable of efficiently dealing with continuous-time graphs represented as a stream of node- or edge-wise events is still an open research question.
#deep-learning #representation-learning #network-science #graph-neural-networks #geometric-deep-learning #deep learning
Welcome back to my series _Neural Networks Intuitions. _In this ninth segment, we will be looking into deep distance metric learning, the motivation behind using it, wide range of methods proposed and its applications.
Note: All techniques discussed in this article comes under Deep Metric Learning (DML) i.e distance metric learning using neural networks.
Distance Metric Learning means learning a distance in a low dimensional space which is consistent with the notion of semantic similarity. (as given in [No Fuss Distance Metric Learning using Proxies])
What does the above statement mean w.r.t image domain?
It means learning a distance in a low dimensional space(non-input space) such that similar images in the input space result in similar representation(low distance) and dissimilar images result in varied representation(high distance).
Okay, this sounds exactly what a classifier does. Isn’t it? Yes.
So how is this different from supervised image classification? Why different terminology?
Metric learning addresses the problem of open-set setup in machine learning i.e generalize to new examples at test time.
This is not possible by a feature-extractor followed by fully connected layer Classification network.
This is a very important question. The answer is as follows:
#metric-learning #deep-learning #siamese-networks #triplet-loss #representation-learning #deep learning
Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources
In this post, I will explain my experience over the course of a year of working with Reinforcement Learning (RL) on autonomous robotics manipulation. It is always hard to start a big project which requires many moving parts. It was undoubtedly the same in this project. I want to pass the knowledge I gathered through this process to help others overcome the initial inertia.
In the beginning, it was tough for me to judge the difficulty of different components of the problem. I underestimated the effort required for some parts and overestimated others. I will explain how RL can be cumbersome and straightforward to work with at the same time.
The below video shows how our agent can transform the learned policy to different domains and different environments.
#reinforcement-learning #artificial-intelligence #computer-vision #deep-learning #openai #neural-networks #self-learning #auto-robotic-manipulation
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