Marktechpost

MAY 14, 2024

Deep learning is crucial in today’s age as it powers advancements in artificial intelligence, enabling applications like image and speech recognition, language translation, and autonomous vehicles. Additionally, it offers insights into the diverse range of deep learning techniques applied across various industrial sectors.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. I also wrote a Medium article about this package in the past to illustrate its use with image classification models.

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Towards AI

SEPTEMBER 10, 2023

What I’ve learned from the most popular DL course Photo by Sincerely Media on Unsplash I’ve recently finished the Practical Deep Learning Course from Fast.AI. So you definitely can trust his expertise in Machine Learning and Deep Learning. There’s an excellent article about it as well.

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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Towards AI

AUGUST 5, 2024

comparison method, cost approach or expert evaluation), machine learning and deep learning models offer new alternatives. In this article, I will give you a simple 10-minute introduction to the most important deep learning models that are frequently used in recent research (see reference) to predict the prices of used cars.

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Towards AI

NOVEMBER 19, 2023

Photo by Pietro Jeng on Unsplash Deep learning is a type of machine learning that utilizes layered neural networks to help computers learn from large amounts of data in an automated way, much like humans do. In this article, we will breakdown each concept in greater detail.

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. It is a tremendous tool with the ability to completely alter numerous sectors.

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Heartbeat

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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AssemblyAI

DECEMBER 7, 2023

This article delves into the key findings from this recent research. Enjoyed this article? Check out our recent articles to learn about: Combining Speech Recognition and Diarization in one model Retrieval Augmented Generation on audio data with LangChain and Chroma What is Residual Vector Quantization?

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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Heartbeat

NOVEMBER 14, 2023

For many years, gradient-boosting models and deep-learning solutions have won the lion's share of Kaggle competitions. XGBoost is not limited to machine learning tasks, as its incredible power can be harnessed when harmonized with deep learning algorithms. " Nuclear Engineering and Technology 53, no.

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Mlearning.ai

DECEMBER 13, 2023

Mastering Deep Learning and AI Interview Questions: What You Need to Know Image created by the author on Canva Knowledge is power, but enthusiasm pulls the switch.” Ever wondered what it takes to excel in deep learning interviews? Explain the vanishing and exploding gradient problems. said Ivern Ball.

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Analytics Vidhya

DECEMBER 3, 2021

This article was published as a part of the Data Science Blogathon. Introduction In this article, I will attempt to explain all of the ideas that you should be familiar with about databases. The post Want to Survive in Data Science: Understand the Basics of Relational Databases appeared first on Analytics Vidhya.

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Heartbeat

NOVEMBER 29, 2023

Two names stand out prominently in the wide realm of deep learning: TensorFlow and PyTorch. These strong frameworks have changed the field, allowing researchers and practitioners to create and deploy cutting-edge machine learning models. TensorFlow and PyTorch present distinct routes to traverse.

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Analytics Vidhya

AUGUST 26, 2021

This article was published as a part of the Data Science Blogathon “You can have data without information but you cannot have information without data” – Daniel Keys Moran Introduction If you are here then you might be already interested in Machine Learning or Deep Learning so I need not explain what it is?

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ODSC - Open Data Science

OCTOBER 17, 2023

But without a strong understanding of deep learning, you’ll have a difficult time getting the most out of the cutting-edge developments in the industry. At ODSC West this October 30th to November 2nd, you’ll build the core knowledge and skills you need with the sessions in the deep learning track , such as the ones listed below.

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Heartbeat

OCTOBER 18, 2023

In this article, we embark on a journey to explore the transformative potential of deep learning in revolutionizing recommender systems. However, deep learning has opened new horizons, allowing recommendation engines to unravel intricate patterns, uncover latent preferences, and provide accurate suggestions at scale.

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Mlearning.ai

JANUARY 3, 2024

word2vec dl4ee: deep learning for electrical engineers Why another article for word2vec? There are many articles discussing word2vec’s techniques. This article starts with the bigger picture of how it serves as the first step into the Natural Language Processing world. This is beyond the scope of the article.

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AssemblyAI

NOVEMBER 21, 2022

At the bedrock of the Deep Learning that powers incredible technologies like text-to-image models lies matrix multiplication. Regardless of the specific architecture employed, (nearly) every Neural Network relies on efficient matrix multiplication to learn and infer. Enjoyed this article?

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Heartbeat

APRIL 23, 2023

Photo by RetroSupply on Unsplash Introduction Deep learning has been widely used in various fields, such as computer vision, NLP, and robotics. The success of deep learning is largely due to its ability to learn complex representations from data using deep neural networks. What is Epistemic Uncertainty?

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Heartbeat

MAY 2, 2023

Artificial intelligence has undergone a revolution thanks to deep learning. Deep learning allows machines to learn from vast amounts of data and carry out complex tasks that were previously only considered possible by humans (like translation between languages, recognizing objects etc.).

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Heartbeat

OCTOBER 3, 2023

Getting familiar with memmap() Photo by Heliberto Arias on Unsplash In the previous article , we demonstrated the benefits and ease with which someone could integrate memory mapping into their workflow. One way we can do this is by taking the binary with information of an “X_train” array in the previous article. It occupies 805.4

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Heartbeat

NOVEMBER 21, 2023

Computer vision, the field dedicated to enabling machines to perceive and understand visual data, has witnessed a monumental shift in recent years with the advent of deep learning. Photo by charlesdeluvio on Unsplash Welcome to a journey through the advancements and applications of deep learning in computer vision.

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John Snow Labs

APRIL 30, 2024

In this article, we will explore the significance of table extraction and demonstrate the application of John Snow Labs’ NLP library with visual features installed for this purpose. The ImageTableDetector is a deep-learning model that identifies tables within images. Let me explain what is happening in the pipeline.

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Heartbeat

SEPTEMBER 27, 2023

Dealing with your memory constraints Photo by Ian Talmacs on Unsplash In the previous article , we discussed the possibility of having memory constraints when trying to get into deep learning. I mentioned Numpy’s memory mapping; in this article, we go deeper into the topic and offer an excellent guide to get you started.

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ODSC - Open Data Science

APRIL 19, 2023

Deep learning continues to be a hot topic as increased demands for AI-driven applications, availability of data, and the need for increased explainability are pushing forward. So let’s take a quick dive and see some big sessions about deep learning coming up at ODSC East May 9th-11th.

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Viso.ai

FEBRUARY 8, 2024

introduced deep belief networks (DBNs) in 2006. These deep learning algorithms consist of latent variables and use them to learn underlying patterns within the data. Deep belief networks played a vital role in the growth and evolution of deep learning, but they are rarely ever used today.

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Marktechpost

JULY 6, 2023

This is where Interpretable (IAI) and Explainable (XAI) Artificial Intelligence techniques come into play, and the need to understand their differences become more apparent. On the other hand, explainable AI models are very complicated deep learning models that are too complex for humans to understand without the aid of additional methods.

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Heartbeat

FEBRUARY 22, 2023

Deep learning for feature extraction, ensemble models, and more Photo by DeepMind on Unsplash The advent of deep learning has been a game-changer in machine learning, paving the way for the creation of complex models capable of feats previously thought impossible.

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Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? In this article, we’ll look at the state of the traditional machine learning landscape concerning modern generative AI innovations. What is Traditional Machine Learning? What are its Limitations?

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Heartbeat

MARCH 28, 2023

A complete guide to building a deep learning project with PyTorch, tracking an Experiment with Comet ML, and deploying an app with Gradio on HuggingFace Image by Freepik AI tools such as ChatGPT, DALL-E, and Midjourney are increasingly becoming a part of our daily lives. These tools were developed with deep learning techniques.

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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How do we deal with this?

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Heartbeat

JANUARY 31, 2023

In deep learning, activation functions add non-linearity to the neural networks. Conclusion That’s all from this article. In this article, we discussed various non-linear activation functions with their mathematical formulas. We also discussed the use case of the activation functions.

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Analytics Vidhya

OCTOBER 10, 2022

This article was published as a part of the Data Science Blogathon. Introduction In this article, we will create a Mask v/s No Mask classifier using CNN and Machine Learning Classifiers. We will learn everything from scratch, and I will explain every […].

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IBM Journey to AI blog

JUNE 26, 2023

Possibilities are growing that include assisting in writing articles, essays or emails; accessing summarized research; generating and brainstorming ideas; dynamic search with personalized recommendations for retail and travel; and explaining complicated topics for education and training. What is watsonx.governance?

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AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). In this post, we illustrate the use of Clarify for explaining NLP models.

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