Agnes  Sauer

Agnes Sauer

1595414880

The Unreasonable Progress of Deep Neural Networks

Humans have a lot of senses, and yet our sensory experiences are typically dominated by vision. With that in mind, perhaps it is unsurprising that the vanguard of modern machine learning has been led by computer vision tasks. Likewise, when humans want to communicate or receive information, the most ubiquitous and natural avenue they use is language. Language can be conveyed by spoken and written words, gestures, or some combination of modalities, but for the purposes of this article we’ll focus on the written word (although many of the lessons here overlap with verbal speech as well).

Over the years we’ve seen the field of natural language processing (aka NLP, not to be confused with that NLP) with deep neural networks follow closely on the heels of progress in deep learning for computer vision. With the advent of pre-trained generalized language models, we now have methods for transfer learning to new tasks with massive pre-trained models like GPT-2, BERT, and ELMO. These and similar models are doing real work in the world, both as a matter of everyday course (translation, transcription, etc.), and discovery at the frontiers of scientific knowledge (e.g. predicting advances in material science from publication text [pdf]).

Mastery of language both foreign and native has long been considered an indicator of learned individuals; an exceptional writer or a person that understands multiple languages with good fluency is held in high-esteem, and is expected to be intelligent in other areas as well. Mastering any language to native-level fluency is difficult, imparting an elegant style and/or exceptional clarity even more so. But even typical human proficiency demonstrates an impressive ability to parse complex messages while deciphering substantial coding variations across context, slang, dialects, and the unshakeable confounders of language understanding: sarcasm and satire.

Understanding language remains a hard problem, and despite widespread use in many areas, the challenge of language understanding with machines still presents plenty of unsolved problems. Consider the following ambiguous and strange word or phrase pairs. Ostensibly the members of each pair have the same meaning, but undoubtedly convey distinct nuance. For many of us the only nuance may be a disregard for precision of grammar and language, but refusing to acknowledge common use meanings mostly makes a language model look foolish.

Couldn’t care less = (?)Could care less

Irregardless= (?)Regardless

Literally = (?)Figuratively

Dynamical= (?)Dynamic

Primer: Generalization and Transfer Learning

Much of the modern success of deep learning has been due to the utility of transfer learning. Transfer learning allows practitioners to leverage a model’s previous training experience to more quickly learn a novel task. With the raw parameter counts and computational requirements of training state of the art deep networks, transfer learning is essential for the accessibility and efficiency of deep learning in practice. If you are already familiar with the concept of transfer learning, skip ahead to the next section to have a look at the succession of deep NLP models over time.

Transfer learning is a process of fine-tuning: rather than training an entire model from scratch, re-training only those parts of the model which are task-specific can save time and energy of both computational and engineering resources. This is the “don’t be a hero” mentality espoused by Andrej Karpathy, Jeremy Howard, and many others in the deep learning community.

Fundamentally, transfer learning involves retaining the low-level, generic components of a model while only re-training those parts of the model that are specialized. It’s also sometimes advantageous to train the entire pre-trained model after only re-initializing a few task-specific layers.

Image for post

A deep neural network can typically be separated into two sections: an encoder, or feature extractor, that learns to recognize low-level features, and a decoder which transforms those features to a desired output. This cartoon example is based on a simplified network for processing images, with the encoder made up of convolutional layers and the decoder consisting of a few fully connected layers, but the same concept can easily be applied to natural language processing as well.

In deep learning models there is often a distinction between the encoder, a stack of layers that mainly learns to extract low-level features, and the decoder, the portion of the model that transforms the feature output from the encoder into classifications, pixel segmentations, next-time-step predictions, and so on. Taking a pre-trained model and initializing and re-training a new decoder can achieve state-of-the-art performance in far less training time. This is because lower-level layers tend to learn the most generic features, characteristics like edges, points, and ripples in images (i.e. Gabor filters in image models). In practice, choosing the cutoff between encoder and decoder is more art than science, but see Yosinki et al. 2014 where researchers quantified the transferability of features at different layers.

#artificial-intelligence #deep-learning #ai #neural-networks #deep learning

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The Unreasonable Progress of Deep Neural Networks
Agnes  Sauer

Agnes Sauer

1595414880

The Unreasonable Progress of Deep Neural Networks

Humans have a lot of senses, and yet our sensory experiences are typically dominated by vision. With that in mind, perhaps it is unsurprising that the vanguard of modern machine learning has been led by computer vision tasks. Likewise, when humans want to communicate or receive information, the most ubiquitous and natural avenue they use is language. Language can be conveyed by spoken and written words, gestures, or some combination of modalities, but for the purposes of this article we’ll focus on the written word (although many of the lessons here overlap with verbal speech as well).

Over the years we’ve seen the field of natural language processing (aka NLP, not to be confused with that NLP) with deep neural networks follow closely on the heels of progress in deep learning for computer vision. With the advent of pre-trained generalized language models, we now have methods for transfer learning to new tasks with massive pre-trained models like GPT-2, BERT, and ELMO. These and similar models are doing real work in the world, both as a matter of everyday course (translation, transcription, etc.), and discovery at the frontiers of scientific knowledge (e.g. predicting advances in material science from publication text [pdf]).

Mastery of language both foreign and native has long been considered an indicator of learned individuals; an exceptional writer or a person that understands multiple languages with good fluency is held in high-esteem, and is expected to be intelligent in other areas as well. Mastering any language to native-level fluency is difficult, imparting an elegant style and/or exceptional clarity even more so. But even typical human proficiency demonstrates an impressive ability to parse complex messages while deciphering substantial coding variations across context, slang, dialects, and the unshakeable confounders of language understanding: sarcasm and satire.

Understanding language remains a hard problem, and despite widespread use in many areas, the challenge of language understanding with machines still presents plenty of unsolved problems. Consider the following ambiguous and strange word or phrase pairs. Ostensibly the members of each pair have the same meaning, but undoubtedly convey distinct nuance. For many of us the only nuance may be a disregard for precision of grammar and language, but refusing to acknowledge common use meanings mostly makes a language model look foolish.

Couldn’t care less = (?)Could care less

Irregardless= (?)Regardless

Literally = (?)Figuratively

Dynamical= (?)Dynamic

Primer: Generalization and Transfer Learning

Much of the modern success of deep learning has been due to the utility of transfer learning. Transfer learning allows practitioners to leverage a model’s previous training experience to more quickly learn a novel task. With the raw parameter counts and computational requirements of training state of the art deep networks, transfer learning is essential for the accessibility and efficiency of deep learning in practice. If you are already familiar with the concept of transfer learning, skip ahead to the next section to have a look at the succession of deep NLP models over time.

Transfer learning is a process of fine-tuning: rather than training an entire model from scratch, re-training only those parts of the model which are task-specific can save time and energy of both computational and engineering resources. This is the “don’t be a hero” mentality espoused by Andrej Karpathy, Jeremy Howard, and many others in the deep learning community.

Fundamentally, transfer learning involves retaining the low-level, generic components of a model while only re-training those parts of the model that are specialized. It’s also sometimes advantageous to train the entire pre-trained model after only re-initializing a few task-specific layers.

Image for post

A deep neural network can typically be separated into two sections: an encoder, or feature extractor, that learns to recognize low-level features, and a decoder which transforms those features to a desired output. This cartoon example is based on a simplified network for processing images, with the encoder made up of convolutional layers and the decoder consisting of a few fully connected layers, but the same concept can easily be applied to natural language processing as well.

In deep learning models there is often a distinction between the encoder, a stack of layers that mainly learns to extract low-level features, and the decoder, the portion of the model that transforms the feature output from the encoder into classifications, pixel segmentations, next-time-step predictions, and so on. Taking a pre-trained model and initializing and re-training a new decoder can achieve state-of-the-art performance in far less training time. This is because lower-level layers tend to learn the most generic features, characteristics like edges, points, and ripples in images (i.e. Gabor filters in image models). In practice, choosing the cutoff between encoder and decoder is more art than science, but see Yosinki et al. 2014 where researchers quantified the transferability of features at different layers.

#artificial-intelligence #deep-learning #ai #neural-networks #deep learning

Vaughn  Sauer

Vaughn Sauer

1621440840

Ultimate Guide for Deep Learning with Neural Network in 2021

In deep learning with Keras, you don’t have to code a lot, but there are a few steps on which you need to step over slowly so that in the near future, you can create your models. The flow of modelling is to load data, define the Keras model, compile the Keras model, fit the Keras model, evaluate it, tie everything together, and make the predictions out of it.

But at times, you might find it confusing because of not having a good hold on the fundamentals of deep learning. Before starting your new deep learning with Keras project, make sure to go through this ultimate guide which will help you in revising the fundamentals of deep learning with Keras.

In the field of Artificial Intelligence, deep learning has become a buzzword which always finds its way in various conversations. When it comes to imparting intelligence to the machines, it has been since many years that we used Machine Learning (ML).

But, considering the current period, due to its supremacy in predictions, deep learning with Keras has become more liked and famous as compared to the old and traditional ML techniques.

Deep Learning

Machine learning has a subset in which the Artificial Neural Networks (ANN) is trained with a large amount of data. This subset is nothing but deep learning. Since a deep learning algorithm learns from experience, it performs the task repeatedly; every time it tweaks it a little intending to improve the outcome.

It is termed as ‘deep learning’ because the neural networks have many deep layers which enables learning. Deep learning can solve any problem in which thinking is required to figure out the problem.

**Keras **

There are many APIs, frameworks, and libraries available to get started with deep learning. But here’s why deep learning with Keras is beneficial. Keras is a high-level neural network application programming interface (API) which runs on the top of TensorFlow – which is an end-to-end machine learning platform and is an open-source. Not just Tensorflow, but also CNTK, Theano, PlaidML, etc.

It helps in commoditizing artificial intelligence (AI) and deep learning. The coding in Keras is portable, it means that using Keras you can implement a neural network while using Theano as a backend and then subsequently run it on Tensorflow by specifying the backend. Also further, it is not mandatory rather, not needed at all to change the code.

If you are wondering why deep learning is an important term in Artificial Intelligence or if you are lagging motivation to start learning deep learning with Keras, this google trends snap shows how people’s interest in deep learning has been growing steadily worldwide for the last few years.

#deep learning #deep learning with neural network #neural network

Angela  Dickens

Angela Dickens

1598313600

Introduction to Neural Networks

There has been hype about artificial intelligence, machine learning, and neural networks for quite a while now. I have been working on these things for over a year now so I would like to share some of my knowledge and give my point of view on Neural networks. This will not be a math-heavy introduction because I just want to build the idea here.

I will start from the neural network and then I will explain every component of a neural network. If you feel like something is not right or need any help with any of this, Feel free to contact me, I will be happy to help.


When to use the Neural Network?

Let’s assume we want to solve a problem where you are given some set of images and you have to build an automated system that can categories each of those images to its correct label.

The problem looks simple but how do we come with some logic using raw pixel values and target labels. We can try comparing pixels and edges but we won’t be able to come with some idea which can do this task effectively or say the accuracy of 90% or more.

When we have this kind of problem where we have high dimensional data like Images and we don’t know the relationship between Input(Images) and the Output(Labels), In this kind of scenario we should use Neural Networks.ư

What is the Neural network?

Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain

#artificial-intelligence #gradient-descent #artificial-neural-network #deep-learning #neural-networks #deep learning

Sofia  Maggio

Sofia Maggio

1626106680

Neural networks forward propagation deep dive 102

Forward propagation is an important part of neural networks. Its not as hard as it sounds ;-)

This is part 2 in my series on neural networks. You are welcome to start at part 1 or skip to part 5 if you just want the code.

So, to perform gradient descent or cost optimisation, we need to write a cost function which performs:

  1. Forward propagation
  2. Backward propagation
  3. Calculate cost & gradient

In this article, we are dealing with (1) forward propagation.

In figure 1, we can see our network diagram with much of the details removed. We will focus on one unit in level 2 and one unit in level 3. This understanding can then be copied to all units. (ps. one unit is one of the circles below)

Our goal in forward prop is to calculate A1, Z2, A2, Z3 & A3

Just so we can visualise the X features, see figure 2 and for some more info on the data, see part 1.

Initial weights (thetas)

As it turns out, this is quite an important topic for gradient descent. If you have not dealt with gradient descent, then check this article first. We can see above that we need 2 sets of weights. (signified by ø). We often still calls these weights theta and they mean the same thing.

We need one set of thetas for level 2 and a 2nd set for level 3. Each theta is a matrix and is size(L) * size(L-1). Thus for above:

  • Theta1 = 6x4 matrix

  • Theta2 = 7x7 matrix

We have to now guess at which initial thetas should be our starting point. Here, epsilon comes to the rescue and below is the matlab code to easily generate some random small numbers for our initial weights.

function weights = initializeWeights(inSize, outSize)
  epsilon = 0.12;
  weights = rand(outSize, 1 + inSize) * 2 * epsilon - epsilon;
end

After running above function with our sizes for each theta as mentioned above, we will get some good small random initial values as in figure 3

. For figure 1 above, the weights we mention would refer to rows 1 in below matrix’s.

Now, that we have our initial weights, we can go ahead and run gradient descent. However, this needs a cost function to help calculate the cost and gradients as it goes along. Before we can calculate the costs, we need to perform forward propagation to calculate our A1, Z2, A2, Z3 and A3 as per figure 1.

#machine-learning #machine-intelligence #neural-network-algorithm #neural-networks #networks

Alec  Nikolaus

Alec Nikolaus

1602261660

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