James  Watson

James Watson


ResNet Architecture and Residual Block Explained - Neural Networks and Deep Learning

In this Neural Networks and Deep Learning Tutorial, we will talk about the ResNet Architecture. Residual Neural Networks are often used to solve computer vision problems and consist of several residual blocks. We will walk about what a residual block is and compare it to the architecture of a standard convolutional neural network. I’ll show you how you can use the pre-trained ResNets from Keras and TensorFlow.

GitHub: https://github.com/niconielsen32​

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#tensorflow #keras #deep-learning

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ResNet Architecture and Residual Block Explained - Neural Networks and Deep Learning
Alec  Nikolaus

Alec Nikolaus


Deep Learning Explained in Layman's Terms

In this post, you will get to learn deep learning through a simple explanation (layman terms) and examples.

Deep learning is part or subset of machine learning and not something that is different than machine learning. Many of us, when starting to learn machine learning, try and look for the answers to the question, “What is the difference between machine learning and deep learning?” Well, both machine learning and deep learning are about learning from past experience (data) and make predictions on future data.

Deep learning can be termed as an approach to machine learning where learning from past data happens based on artificial neural networks (a mathematical model mimicking the human brain). Here is the diagram representing the similarity and dissimilarity between machine learning and deep learning at a very high level.

#machine learning #artificial intelligence #deep learning #neural networks #deep neural networks #deep learning basics

Marget D

Marget D


Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Deep learning on graphs: successes, challenges, and next steps

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.

complex social network

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases [2], has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [3] if not two [4], 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 manifoldsdrug 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 [5] 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 [6] 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 [7].

**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

Angela  Dickens

Angela Dickens


Explain Deep Learning Neural Networks to your grandma

“You do not really understand something unless you can explain it to your grandmother”

Not sure where this quote originally came from, it is sometimes kind of half-attributed to Albert Einstein.

Anyway, this post is my attempt of explaining (to myself and others) how neural networks (NN) algorithm works in a simple, novice, straightforward, and high-level fashion, without any formulas, equations, or codes. I am fully aware that there might be some inaccuracies in the text below, but this is naturally to happen in order to avoid complicated explanations and to just keep things simple, otherwise your grandma won’t understand…

Before we begin, I would like to acknowledge fast.ai, founded by Jeremy Howard and Rachel Thomas, which is a non-profit research group focused on deep learning and artificial intelligence. I have learnt and still learning a lot from their free online courses.

Oh… and I wrote a summary of this post at the end in case you want to skip to it.

OK, I assume that if you read this post you have at least some familiarity with NN, so I’ll save you the introduction about how NN became popular in recent years and how they can help you solve problems.

So how does NN algorithm works?

In one word: math.

In two words: matrix multiplication.

If your grandma did not understand this explanation then you can tell her that we start with an input layer, which is the input data for a NN model.

#neural-networks #machine-learning #deep-learning #deep learning

Noah  Rowe

Noah Rowe


Deep Learning 101 —  Neural Networks Explained

The past few decades have witnessed a massive boom in the penetration as well as the power of computation, and amidst this information revolution, AI has radically changed the way people and businesses make decisions. The poster-boys for these cutting edge AI solutions — Deep Learning and Neural Networks, despite their hype, are understood only by a few.

In this article, I will try to explain what Neural Networks are, and how they function, and their strengths and limitations. And hopefully, convince you that they’re much simpler than they’re thought to be.

_NOTE: Deep Learning is an subset of Machine Learning, and if you’re unsure how that works, I’d advise you check out _[this article]

Before diving right in, let’s explore a simpler problem, to establish a few fundamental concepts.

Logistic Regression

Assume we’re a credit card company targeting people without an adequate credit history. Using the historical data of its users, we want to decide whether a new applicant should be given the credit card or not.

Assume the data we have to make the decision is the following.

· Age

· Salary

· Education

· City

· Company

· Designation

Note: I realize that the latter 4 are “qualitative” variables, but for this illustration, let’s assume we can somehow quantify them

The final decision is a Yes or No, thus making it a classification problem. To solve this we’ll make a small modification to our linear regression approach — After multiplying the variables with their weights (the technically correct name of the slopes or coefficients we used in Linear Regression) and adding them, (and the constant), the result can be any number, and to convert it into a Yes/No decision, we will have to process it further, i.e. pass it through a function.

One option for g could be a simple Threshold function:

![Threshold Function]

Threshold Function

Ie. The decision being No if the sum product of variables and weights is z<0 and Yes if Z>=0

However often, instead of absolute Yes or No, we may want the probability of the result, and also because the algorithm computing the optimum weights work better for smooth (or in technical terms, differentiable) functions, we use a different function called the sigmoid.

Sigmoid Function

Tying it all together, for a new customer, we can input the different variables (age, salary etc.), sum their product with them with the weights, and apply the sigmoid function to get the probability of his eligibility for the card, and this whole process is called Logistic Regression.

Activation Functions

The reason for taking the detour through Logistic Regression was to introduce the concept of activation functions. The threshold or sigmoid function used in Logistic Regression were examples of activation functions.

Any function applied on the sum-product of variables and weights(note that there will be a corresponding weight for each variable) is known as an activation function. Apart from the Threshold and sigmoid functions discussed above, a few others that are commonly used

ReLU (Rectified Linear Unit):

![ReLU graph]

This means returning Z as it is if it’s positive, and returning 0 otherwise.

Tanh (Hyperbolic Tan):

Tanh graph

This is sigmoid stretched downwards on the y-axis so that it’s centred at (0,0) instead of (0,0.5)

These, along with their small variations are the most commonly used activation functions in Neural Networks because of reasons beyond the scope of this article.

#data-science #neural-networks #artificial-intelligence #deep-learning #machine-learning #deep learning