The intuition of Triplet Loss

Many of us feel Machine learning as a black box which takes some input and gives out some amazing output. In recent years, this same Black box is creating wonders by acting as a mimic of Human in the respective fields where it is being used.

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But from my experience, it is very interesting, fun and sometimes frustrating(😜) as we go deep into this Black Box. This black box achieved so many things which none of us expected a decade ago. The most fun part of ML is understanding the way this black box do things behind the scenes which makes it create wonders.

#triplet-loss #machine-learning #loss-function #face-net

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The intuition of Triplet Loss

The intuition of Triplet Loss

Many of us feel Machine learning as a black box which takes some input and gives out some amazing output. In recent years, this same Black box is creating wonders by acting as a mimic of Human in the respective fields where it is being used.

Image for post

But from my experience, it is very interesting, fun and sometimes frustrating(😜) as we go deep into this Black Box. This black box achieved so many things which none of us expected a decade ago. The most fun part of ML is understanding the way this black box do things behind the scenes which makes it create wonders.

#triplet-loss #machine-learning #loss-function #face-net

David mr

David mr

1623621600

ALTCOIN DUMP EXPLAINED AND AVOID MORE LOSSES!

This is how you can trade to avoid any severe Altcoin losses NOW! The Sniper and Crypto Man Ran explain this dump in detail!

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#bitcoin #blockchain #altcoin #altcoin dump explained #losses #altcoin dump explained and avoid more losses

Win at Customer Service with Intuitive Customer Support Chatbots

COVID-19 has upended almost all businesses, and one of the worst-hit sectors is customer service .

On the one hand, anxious and emotionally-charged customers are calling companies night-and-day to make travel cancellations, ask for refunds, inquire about bill payment extensions, and so on. Naturally, the volume and intensity of calls have sky-rocketed, leaving **customer support ** teams to fend for themselves. Additionally, the lack of proper infrastructure is making things worse for the team and the company at large. The logical solution?

It would be fair to assume that the pandemic is _forcing _ companies to move towards online support such as the use of AI chatbots , live chat, etc. – a great move, in my opinion. In this blog, we will look at the top-5 ways to enhance customer service for support agents working from home by using chatbots. Let’s jump right in.

#chatbots #latest news #customer service #support #win at customer service with intuitive customer support chatbots #intuitive

Neural Networks Intuitions: 9. Distance Metric 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:

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.

Why?

This is a very important question. The answer is as follows:

  1. A classifier learns**class-specific features and not necessarily generic features.**
  2. A classifier with a standard cross entropy loss maximizes inter-class distances such that the features before FC layer are linearly separable.

#metric-learning #deep-learning #siamese-networks #triplet-loss #representation-learning #deep learning

Uriah  Dietrich

Uriah Dietrich

1615957260

Ultimate Guide To Loss functions In Tensorflow Keras API With Python Implementation

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Ledger App Khatabook Helps SMBs To Keep Up With India’s Digital Aspirations

We have already covered the PyTorch loss functions implementations in our previous article, now we are heading forward to the other libraries that have been used more widely than PyTorch, today we are going to discuss the loss functions supported by the Tensorflow library, there are almost 15 different kinds of loss functions supported by TensorFlow, some of them are available in both Class and functions format you can call them as a class method or as a function.

The class handles enable you to pass configuration arguments to the constructor (e.g. loss_fn = CategoricalCrossentropy(from_logits=True)), and they perform reduction by default when used in a standalone way they are defined separately, all the loss functions are available under Keras module, exactly like in PyTorch all the loss functions were available in Torch module, you can access Tensorflow loss functions by calling tf.keras.losses method.

Table of contents
Tensorflow Keras Loss functions
Implementation

  1. Binary Cross-Entropy(BCE) loss
  2. Categorical Crossentropy loss
  3. Sparse Categorical Crossentropy loss
  4. Poisson loss
  5. Kullback-Leibler Divergence loss
    KL(P || Q) = – sum x in X P(x) * log(Q(x) / P(x))
  6. Mean Squared Error(MSE)
  7. MeanAbsoluteError
  8. Mean Absolute Percentage Error(MAPE)
  9. Mean Squared Logarithmic Error(MSLE)
  10. CosineSimilarity loss
  11. Huber loss
  12. LogCosh loss
  13. Hinge loss
  14. Squared Hinge loss
  15. CategoricalHinge loss
    Conclusion
    Read more:

#uncategorized #keras loss functions #python for data science #tensorflow #tensorflow loss fucntions