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When we think of Artificial Intelligence, it becomes almost overwhelming to wrap our brains around complex terms like Machine Learning, Deep Learning, and Natural Language Processing (NLP). After all, these new-age disciplines are much more advanced and intricate than anything we’ve ever seen. This is primarily why people tend to use AI terminologies synonymously, sparking a debate of sorts between different concepts of Data Science.

One such trending debate is that of Deep Learning vs. NLP. While Deep Learning and NLP fall under the broad umbrella of Artificial Intelligence, the difference between Deep Learning and NLP is pretty stark!

In this post, we’ll take a detailed look into the Deep Learning vs. NLP debate, understand their importance in the AI domain, see how they associate with one another, and learn about the differences between Deep Learning and NLP.

So, without further ado, let’s get straight into it!

Deep Learning is a branch of Machine Learning that leverages artificial neural networks (ANNs)to simulate the human brain’s functioning. An artificial neural network is made of an interconnected web of thousands or millions of neurons stacked in multiple layers, hence the name Deep Learning.

#artificial intelligence #deep learning #deep learning vs nlp #nlp

1620755822

When we think of Artificial Intelligence, it becomes almost overwhelming to wrap our brains around complex terms like Machine Learning, Deep Learning, and Natural Language Processing (NLP). After all, these new-age disciplines are much more advanced and intricate than anything we’ve ever seen. This is primarily why people tend to use AI terminologies synonymously, sparking a debate of sorts between different concepts of Data Science.

One such trending debate is that of Deep Learning vs. NLP. While Deep Learning and NLP fall under the broad umbrella of Artificial Intelligence, the difference between Deep Learning and NLP is pretty stark!

In this post, we’ll take a detailed look into the Deep Learning vs. NLP debate, understand their importance in the AI domain, see how they associate with one another, and learn about the differences between Deep Learning and NLP.

So, without further ado, let’s get straight into it!

Deep Learning is a branch of Machine Learning that leverages artificial neural networks (ANNs)to simulate the human brain’s functioning. An artificial neural network is made of an interconnected web of thousands or millions of neurons stacked in multiple layers, hence the name Deep Learning.

#artificial intelligence #deep learning #deep learning vs nlp #nlp

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

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

1620778500

Machine learning and Deep learning both are the buzzwords in the tech industry. Machine learning and deep learning both are the subdivision of artificial intelligence technology. If we further breakdown, deep learning is a subdivision of machine learning technology.

If you are familiar with the basics of machine learning and deep learning, it is excellent news!

However, if you are new to the AI field, then you must be confused. What is the difference between machine learning and deep learning?

There is nothing to worry about. This article will explain the differences in easy to understand language.

Machine learning is a branch of technology that studies computer algorithms. These algorithms allow the system to learn from data or improve by itself through experience. Machine learning algorithms make predictions or decisions without being explicitly programmed.

#artificial intelligence #comparison #deep learning #machine learning #machine learning vs deep learning

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The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

**Also Read:** Why Deep Learning DevCon Comes At The Right Time

**By Dipanjan Sarkar**

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

**By Divye Singh**

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

**By Dongsuk Hong**

**About:** This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.

#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

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This is the third part of a series of posts showing the improvements in NLP modeling approaches. We have seen the use of traditional techniques like Bag of Words, TF-IDF, then moved on to RNNs and LSTMs. This time we’ll look into one of the pivotal shifts in approaching NLP Tasks — Transfer Learning!

The complete code for this tutorial is available at this Kaggle Kernel

The idea of using Transfer Learning is quite new in NLP Tasks, while it has been quite prominently used in Computer Vision tasks! This new way of looking at NLP was first proposed by Howard Jeremy, and has transformed the way we looked at data previously!

The core idea is two-fold — using generative pre-trained Language Model + task-specific fine-tuning was first explored in ULMFiT (Howard & Ruder, 2018), directly motivated by the success of using ImageNet pre-training for computer vision tasks. The base model is AWD-LSTM.

A Language Model is exactly like it sounds — the output of this model is to predict the next word of a sentence. The goal is to have a model that can understand the semantics, grammar, and unique structure of a language.

**ULMFit** follows three steps to achieve good transfer learning results on downstream language classification tasks:

- General Language Model pre-training: on Wikipedia text.
- Target task Language Model fine-tuning: ULMFiT proposed two training techniques for stabilizing the fine-tuning process.
- Target task classifier fine-tuning: The pretrained LM is augmented with two standard feed-forward layers and a softmax normalization at the end to predict a target label distribution.

**fast.ai**’s motto — Making Neural Networks Uncool again — tells you a lot about their approach ;) Implementation of these models is remarkably simple and intuitive, and with good documentation, you can easily find a solution if you get stuck anywhere. Along with this, and a few other reasons I elaborate below, I decided to try out the ** fast.ai** library which is built on top of PyTorch instead of Keras. Despite being used to working in Keras, I didn’t find it difficult to navigate fast.ai and the learning curve is quite fast to implement advanced things as well!

In addition to its simplicity, there are some advantages of using fast.ai’s implementation -

**Discriminative fine-tuning**is motivated by the fact that different layers of LM capture different types of information (see discussion above). ULMFiT proposed to tune each layer with different learning rates, {η1,…,ηℓ,…,ηL}, where η is the base learning rate for the first layer, ηℓ is for the ℓ-th layer and there are L layers in total.

Weight update for Stochastic Gradient Descent (SGD). ∇θ(ℓ)J(θ) is the gradient of Loss Function with respect to θ(ℓ). η(ℓ) is the learning rate of the ℓ-th layer.

**Slanted triangular learning rates (STLR)**refer to a special learning rate scheduling that first linearly increases the learning rate and then linearly decays it. The increase stage is short so that the model can converge to a parameter space suitable for the task fast, while the decay period is long allowing for better fine-tuning.

Learning rate increases till 200th iteration and then slowly decays. Howard, Ruder (2018) — Universal Language Model Fine-tuning for Text Classification

Let’s try to see how well this approach works for our dataset. I would also like to point out that all these ideas and code are available at fast.ai’s free official course for Deep Learning.

#nlp #machine-learning #transfer-learning #deep-learning #deep learning