Shradha Singh

1611759358

Best 5 Neural Network Models Every AI Professional Needs to Know | DI47 Studio

Ever wondered how DeepMind’s AlphaGo easily defeated Lee Sedol, one of the best Go players.

No one saw it coming. It totally seemed impossible, but with the help of deep learning, anything is possible today.

Being a subset of machine learning, it now lies at the heart of multiple innovations seen across industries. From self-driving cars to image processing and natural language processing – it’s already here. Most often people think that artificial neural networks and deep learning are terms often used interchangeably, which is incorrect. Not all neural networks can be called “deep” with multiple hidden layers and not all deep learning architectures can be called neural networks.

However, we will further talk more briefly about neural networks and how they can be used to solve multiple complex problems. Although you will find many neural networks present out there, we will only be talking about the ones that are commonly used in the current industries.

Let’s look at some of the important neural network models in deep learning:

Deep Belief Network
Deep Belief Network (DBN), with the help of unsupervised machine learning and probabilities, helps generate output. The DBN is different from other models since each layer is orderly regulated and learns the complete input. The DBN encompasses undirected layers, directed layers, and binary latent variables.

In the DBS network, each of the hidden sub-network layers is visible to the next layer. Therefore, enabling a fast layer-by-layer unsupervised training model making contrastive divergence applicable to each of the sub-network. This gets started with the lowest layer that is visible.

Algorithms that are known as greedy learning algorithms are used to train the DBN. These algorithms incorporate the learning one layer at a time. As a result, a different version of data gets added to each layer. Therefore, every layer will use the output from the previous layer to be placed at its input.

Deep Belief Network is highly applicable in the field of motion capture data, image recognition, and video recognition.

#neural network #deep learning #algorithms

What is GEEK

Buddha Community

Best 5 Neural Network Models Every AI Professional Needs to Know | DI47 Studio
bindu singh

bindu singh

1647351133

Procedure To Become An Air Hostess/Cabin Crew

Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

 

Physical and Medical standards –

  • Females must be 157 cm in height and males must be 170 cm in height (for males). This parameter may vary from one airline toward the next.
  • The candidate's body weight should be proportional to his or her height.
  • Candidates with blemish-free skin will have an advantage.
  • Physical fitness is required of the candidate.
  • Eyesight requirements: a minimum of 6/9 vision is required. Many airlines allow applicants to fix their vision to 20/20!
  • There should be no history of mental disease in the candidate's past.
  • The candidate should not have a significant cardiovascular condition.

You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

As can be seen from the preceding information, a 10+2 pass is the minimal educational need for becoming an air hostess in India. So, if you have a 10+2 certificate from a recognized board, you are qualified to apply for an interview for air hostess positions!

You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).

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Shradha Singh

1611759358

Best 5 Neural Network Models Every AI Professional Needs to Know | DI47 Studio

Ever wondered how DeepMind’s AlphaGo easily defeated Lee Sedol, one of the best Go players.

No one saw it coming. It totally seemed impossible, but with the help of deep learning, anything is possible today.

Being a subset of machine learning, it now lies at the heart of multiple innovations seen across industries. From self-driving cars to image processing and natural language processing – it’s already here. Most often people think that artificial neural networks and deep learning are terms often used interchangeably, which is incorrect. Not all neural networks can be called “deep” with multiple hidden layers and not all deep learning architectures can be called neural networks.

However, we will further talk more briefly about neural networks and how they can be used to solve multiple complex problems. Although you will find many neural networks present out there, we will only be talking about the ones that are commonly used in the current industries.

Let’s look at some of the important neural network models in deep learning:

Deep Belief Network
Deep Belief Network (DBN), with the help of unsupervised machine learning and probabilities, helps generate output. The DBN is different from other models since each layer is orderly regulated and learns the complete input. The DBN encompasses undirected layers, directed layers, and binary latent variables.

In the DBS network, each of the hidden sub-network layers is visible to the next layer. Therefore, enabling a fast layer-by-layer unsupervised training model making contrastive divergence applicable to each of the sub-network. This gets started with the lowest layer that is visible.

Algorithms that are known as greedy learning algorithms are used to train the DBN. These algorithms incorporate the learning one layer at a time. As a result, a different version of data gets added to each layer. Therefore, every layer will use the output from the previous layer to be placed at its input.

Deep Belief Network is highly applicable in the field of motion capture data, image recognition, and video recognition.

#neural network #deep learning #algorithms

Murray  Beatty

Murray Beatty

1598606037

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers

Articles

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

Marlon  Boyle

Marlon Boyle

1594366200

Recurrent Neural Networks for Multilabel Text Classification Tasks

The purpose of this project is to build and evaluate Recurrent Neural Networks(RNNs) for sentence-level classification tasks. I evaluate three architectures: a two-layer Long Short-Term Memory Network(LSTM), a two-layer Bidirectional Long Short-Term Memory Network(BiLSTM), and a two-layer BiLSTM with a word-level attention layer. Although they do learn useful vector representation, BiLSTM with attention mechanism focuses on necessary tokens when learning text representation. To that end, I’m using the 2019 Google Jigsaw published dataset on Kaggle labeled “Jigsaw Unintended Bias in Toxicity Classification.” The dataset includes 1,804,874 user comments, with the toxicity level being between 0 and 1. The final models can be used for filtering online posts and comments, social media policing, and user education.

Links

Recurrent Neural Networks Overview

RNNs are neural networks used for problems that require sequential data processing. For instance:

  • In a sentiment analysis task, a text’s sentiment can be inferred from a sequence of words or characters.
  • In a stock prediction task, current stock prices can be inferred from a sequence of past stock prices.

At each time step of the input sequence, RNNs compute the output yt and an internal state update ht using the input xt and the previous hidden-state ht-1. They then pass information about the current time step of the network to the next. The hidden-state ht summarizes the task-relevant aspect of the past sequence of the input up to t, allowing for information to persist over time.

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Recurrent Neural Network

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Recurrent Neural Network

During training, RNNs re-use the same weight matrices at each time step. Parameter sharing enables the network to generalize to different sequence lengths. The total loss is a sum of all losses at each time step, the gradients with respect to the weights are the sum of the gradients at each time step, and the parameters are updated to minimize the loss function.

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forward pass: compute the loss function

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loss function

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Backward Pass: compute the gradients

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gradient equation

Although RNNs learn contextual representations of sequential data, they suffer from the exploding and vanishing gradient phenomena in long sequences. These problems occur due to the multiplicative gradient that can exponentially increase or decrease through time. RNNs commonly use three activation functions: RELU, Tanh, and Sigmoid. Because the gradient calculation also involves the gradient with respect to the non-linear activations, architectures that use a RELU activation can suffer from the exploding gradient problem. Architectures that use Tanh/Sigmoid can suffer from the vanishing gradient problem. Gradient clipping — limiting the gradient within a specific range — can be used to remedy the exploding gradient. However, for the vanishing gradient problem, a more complex recurrent unit with gates such as Gated Recurrent Unit (GRU) or Long Short-Term Memory (LSTM) can be used.

#ai #recurrent-neural-network #attention-network #machine-learning #neural-network