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NLP and Deep Learning for Data Scientists

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Deep learning and natural language processing (NLP and Deep Learning)are as busy as they’ve always been. The most in-demand technologies are deep learning and natural language processing (NLP). Advances in natural language processing and deep learning (NLP and Deep Learning) are produced nearly every day. Despite the fact that quarantine regulations in many nations have hampered numerous businesses, the Machine Learning industry continues to advance.

Aside from the fact that the Covid-19 has caused problems for a number of organizations, new-age tech skills such as Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP and Deep Learning) is in high demand. For budding Data Scientists, here are some must-read publications. In this article, we at Learnbay try to go over some of the most crucial and current breakthroughs.

How Deep Learning can keep you safe

Nukanj Aggarwal, ML Lead at Citizen, compiled a list of instances of how deep learning is being utilized to produce life-changing technology in his article that written. This article, ideally written by Citizen’s Machine Learning Lead, that shows how deep learning is being utilized to produce life-changing (or life-saving) technology.

  • Citizen is nothing but a company that analyses first and foremost responder radio frequencies that using speech-to-text engines as well as convolutional neural networks.
  • Citizen is a real-time emergency as well as safety alert app that quite notifies users of occurrences and crimes that have specially occurred in their neighborhood.
  • The company has been able to expand its apps to a number of US cities.
  • In the coming years, this technology could signify a significant shift in the police and first responder infrastructure.
  • The NLP-driven has the ability to transform the police and response infrastructure dramatically.

The Publication of the Open AI API

The publication of GPT-3 by Open AI was arguably the most significant development in the field of natural language processing this year. The API allows businesses and individuals to integrate OpenAI’s new AI technologies into their products and services. The publication of Open AI’s API, on the other hand, may have gone unnoticed by many.

  • The API’s goal is technically keeps their focus on to provide users with access to future models built by the corporation, such as GPT-3.
  • The API is general-purpose and can be used on nearly any natural language work; its success is inversely proportional to the task’s complexity.
  • This is significant since it represents a departure from the company’s usual practice of open-sourcing its models (as they did with GPT-2)
  • The company discusses why they opted to produce a commercial product this time, why they avoided open-source this time, and how they will manage any API misuse in the post.
  • This official blog discusses how the corporation moved away from open source in order to prevent API exploitation

IBM will no longer offer, develop, or research facial recognition technology

This official blog discusses how the corporation moved away from open source in order to prevent API exploitation. The CEO of IBM publicly indicated in a letter to Congress that the business would certainly be ceasing development as well as service offers of general-purpose facial recognition technologies and methodologies.

  • Artificial intelligence advancements have substantially enhanced facial recognition software during the last decade.
  • This was a significant step forward for the organisation, as well as a strong message to the data science community at large.
  • Face recognition technology will no longer be developed or researched by IBM, according to the company.
  • IBM’s decision to prioritise ethics and safety may have influenced other large IT firms (including Microsoft) to follow suit.
  • They feel that now is the right time to start a national conversation about whether and how domestic law enforcement organisations should use facial recognition methodologies.

Conversational AI: Neural Approaches

It examines neural approaches to conversational AI that have been developed in recent times as well. Audiences are interested in Natural Language Processing and Information Retrieval.

  • The researchers divided into three categories: question answering agents, task-oriented dialogue agents, and chatbots in this paper.
  • It offers a complete overview of the various approaches to conversational AI that have been developed in recent years, including quality assurance, task-oriented, and social bots, as well as a unified view of optimum decision-making.
  • An overview of state-of-the-art neural techniques is offered for each category, along with a comparison of them to traditional approaches, as well
  • Its a discussion of progress made and obstacles still faced, using specific systems and models as case studies and sets.

It offers a coherent perspective as well as a full presentation of the key concepts and insights required to comprehend and develop modern dialogue agents that will be critical in making world knowledge and services accessible to millions of people in natural and intuitive ways.

Language Models Are Unsupervised Multitask Learners

Question answering, machine translation, reading comprehension, and summarization are all examples of natural language processing (NLP) problems that are often ideally tackled using supervised learning on task-specific data models as well.

  • When trained on a new dataset of millions of online pages called WebText, the authors proved that language models began to learn these tasks without any explicit administration as well.
  • The language model’s capacity is nothing but critical to zero-shot task transfer’s effectiveness just because of the increase whilst it certainly enhances performance in a log-linear pattern-wise across tasks.
  • These findings point to a possible avenue for developing language processing algorithms that learn to fulfill tasks based on natural demonstrations.

Generative Pre-Training Improves Language Understanding

The researchers discussed natural language processing and how discriminatively trained models can struggle to perform effectively in this paper published by OpenAI.

  • Most deep learning approaches necessitate a large amount of manually labelled data, which limits their usefulness in many sectors where annotated resources are few.
  • The approach’s effectiveness was technically proved on a numeric of natural language processing criteria, as according to the specific researchers.
  • These target tasks do not have to be in the same domain as the unlabeled corpus in our configuration.

They suggested a broad task-agnostic model that beat discriminatively trained models that use architectures specifically generated for each specific task in around 9 of the 12 tasks that studied, greatly outperforming the state-of-the-art. Their goal is to learn a universal representation that can be used for a variety of tasks with minimum change.

Deep Learning Generalization

Many difficult research areas, like image recognition and natural language processing, have seen considerable success using deep learning.

  • Deep learning has had a substantial impact on the conceptual foundations of Machine Learning and artificial intelligence and has achieved significant practical success.
  • They would demonstrate in this certain Deep Learning Generalization article that deep learning technology nowadays is a strong contender for increasing sensing abilities.

The Model Card Toolkit for Easier Model Transparency Reporting

Transparency in Machine Learning (ML) models is crucial in a range of sectors that affect people’s lives, including healthcare, personal finance, and implementation as well. It gets more difficult to convey the intended use cases and other information to consumers downstream whenever larger and also possibly more and more intricate deep learning models are developed.

  • The details that developers need to assess whether or not a model is appropriate for their use case may vary, as will the information required by downstream users.
  • To help and assess that how to tackle this particular difficulty, as Google researchers ideally developed the “Model Card Toolkit,” which particularly simplifies the creation of model transparency reports.

The Complete Guide to Deep Learning Algorithms

This article, written by Sergios Karagiannakos, the founder of AI Summer, provides a comprehensive guide to deep learning.

  • Deep Learning is getting a lot of traction in both the scientific and corporate worlds.
  • Sergios Karagiannakos, certainly the founder of AI Summer, who has written a comprehensive handbook.
  • More and more businesses are incorporating them into their regular operations. It covers far too many topics, ranging from various types of neural networks to deep learning baselines.

Deepfake Detection Tools and AI-Generated Text

With the widespread dissemination of misinformation on social media, I was alarmed when I noticed it had reached my own inner surrounding. The consequences of such deepfakes have been disastrous, with hacked videos of public personalities circulating, putting their reputations at risk. I wanted to help counteract the nefarious use of these technologies as it has become easier to make deepfakes and manufacture fake articles using AI.

  • Given the catastrophic consequences of deepfakes, many attempts to develop relevant tools to detect them have been attempted, with variable degrees of success.
  • Furthermore, the digital behemoth unveiled a new tool that can detect doctored information and ensure readers of its veracity.
  • This article explains a few easy strategies and browser plugins for detecting deepfakes and AI-generated text.
  • Binghamton University and Intel researchers developed a method that goes beyond deepfake identification to identify the deepfake model behind the hacked video.

GPT-3 Philosophers (updated with replies by GPT-3)

This is a fascinating thinking piece in which nine philosophers go into Open AI’s GPT-3. It’s not only a matter of correcting the linguistic biases that have arisen (or used in training.) This is an intriguing thinking article from Daily Nous, in which nine philosophers delve into Open AI’s GPT-3.

  • It isn’t a case of discovering a technological panacea to eliminate bias.
  • The thought leaders ponder the ethical and moral challenges that technology may raise, as well as the remaining questions that it may raise.

Bridging The Gap Between Training & Inference For Neural Machine Translation

This paper is one of the top NLP papers that published from the premier conference, Association for Computational Linguistics (ACL). Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.

  • This paper bridging The Gap Between Training & Inference For Neural Machine Translation talks about the error accumulation.
  • The researchers certainly addressed such specific problems by sampling context words, not only from the ground truth sequence. But also from the predicted sequence particularly by the model during training, whereas the predicted sequence is technically selected with a sentence-level optimum.
  • In this paper, they address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence.
  • According to the specific researchers, this approach can technically achieve significant improvements in multiple datasets.

The Matrix Calculus You Need For Deep Learning

This document attempts to teach all of the matrix mathematics required to comprehend deep neural network training. Using the automatic differentiation built into modern deep learning libraries. This certainly explains how to become a world-class and relevant deep learning practitioner with only a basic understanding of scalar calculus.

  • They presume you know nothing about arithmetic beyond what you studied in calculus 1 and provide resources to assist you refresh your math skills if necessary.
  • This material is for those who are already familiar with the basics of neural networks and want to deepen their understanding of the underlying math.

You do not need to understand this material before learning to train and use deep learning in practise; rather, this material is for those who are already familiar with the basics of neural networks and want to deepen their understanding of the underlying math.

Final lines

We hope that these articles and instructions on natural language processing and NLP and Deep Learning helped you keep up with some of the major developments in Machine Learning this year. Increased focus with NLP and Deep Learning means more internet materials are available. But a good article is sometimes required to gain a solid understanding of such a complicated and multi-faceted subject. Articles can help you improve your overall data literacy by providing basic background information, such as an introduction to deep learning and natural language processing (NLP) or clarification on significant ideas and real-world illustrations very well. Keep growing, my fellow members of the A.I. community.


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#Data Science#Machine Learning