Machine Learning with Embeddings & Explaining Deep Neural Networks
📢: Maria Perchyk, software engineer
🗣: Machine Learning with Embeddings
Embeddings are one of today’s main concepts in machine learning. They capture the semantics of all kinds of data, such as texts, images and videos. Image classification and sentiment analysis are only two out of many applications. In this talk, I will show you how to visualize, train and apply embeddings for text and image data.
📢: Oliver Zeigermann, developer/data scientist (http://zeigermann.eu/)
:🗣: Explaining Deep Neural Networks using Surrogate Models
Deep Neural Networks are great for learning any form of decision boundaries and also can be made to generalize well. But with a lot of complexity they become black boxes and no one can understand what is going on inside of them. In this talk I will demo the idea of a decision tree as a surrogate model for such a network that gives you a dial between understandability and accuracy
#machine-learning #data-science #python #tensorflow
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
This cheat sheet helps you to choose the proper estimate for the task that is the hardest portion of the work. With modern computer technology, today’s machine learning isn’t like machine learning from the past.
The notion that computer may learn without being trained to do certain tasks came from pattern recognition researchers interested in artificial intelligence sought to explore if computers could learn from the information.
The iterative component of machine education is crucial because they may adjust autonomously when models are exposed to fresh data. From past calculations, they learn to create dependable, repeatable judgments and results. It’s not a new science, but a new one.
The usage of programming and even equipment is automation for computerized commands. AI, again, is the robots’ ability to reproduce human habits and thinking and get more clever all the time. It is important, while a misleadingly sharp computer may learn and modify its job as it receives new information, it cannot completely replace people. Everything is equal, it’s a resource, not a risk.
#artificial-intelligence #machine-learning #deep-learning #big-data #deep learning #machine learning
“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
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.
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.
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:
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.
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.
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
This means returning Z as it is if it’s positive, and returning 0 otherwise.
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
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