Knowledge Hut

JUNE 16, 2023

We all have witnessed how Deep learning has emerged as one of the most promising domains of artificial intelligence, enabling machines to process, analyze and draw insights from vast amounts of data. And hence, it has become significant to master some of the major deep learning tools to work with this concept better.

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U-Next

AUGUST 16, 2022

Introduction: About Deep Learning Python. Initiatives based on Machine Learning (ML) and Artificial Intelligence (AI) are what the future has in store. What Is Deep Learning Python? Python is also intriguing to many developers since it is simple to learn. Deep Learning’s Top Python Libraries.

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DareData

JUNE 11, 2024

In this post, we’ll learn how to train a computer vision model using a convolutional Neural Network in PyTorch PyTorch is currently one of the hottest libraries in the Deep Learning field. For example: We’ve learned the basics about tensors ; We understood how to create our first linear model (regression) using PyTorch.

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Cloudera

APRIL 19, 2021

To make these processes simpler — and to get data scientists working on ML use cases faster — we made it simple to configure and leverage NVIDIA GPUs natively in CML. The problem set, however, hasn’t kept up to the times and modern GPUs and algorithms are now able to solve it faster than it takes you to read this paragraph.

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Knowledge Hut

OCTOBER 30, 2023

In recent years, the field of deep learning has gained immense popularity and has become a crucial subset of artificial intelligence. Data Science aspirants should learn Deep Learning after taking a Data Science certificate online , which would enhance their skillset and create more opportunities for them.

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ProjectPro

MARCH 17, 2021

“Machine Learning” and “Deep Learning” – are two of the most often confused and conflated terms that are used interchangeably in the AI world. However, there is one undeniable fact that both machine learning and deep learning are undergoing skyrocketing growth. respectively.

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U-Next

SEPTEMBER 22, 2022

This is much better than deep learning. . In this administer learning issue, a set of pre-labeled training data is provided to a Machine Learning algorithm. Today, we will investigate this widely used problem using the Kera Open-Source Library for Deep Learning. . Neural Network Architecture .

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ProjectPro

NOVEMBER 30, 2021

Deep learning was developed in the early 1940s to mimic the neural networks of the human brain. However, in the last few decades, deep learning has unleashed itself into the world. 85% of data science platform vendors have the first version of deep learning in products. What does a Deep Learning Engineer do?

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ProjectPro

JANUARY 18, 2022

Perhaps the unwavering emergence of Deep Learning Applications on each passing day is the prove, maybe, we're already lodging in – into an advanced world. According to Markets and Markets, the deep learning application market was worth USD 2.28 billion in 2017 and is anticipated to be worth USD 18.16 And many more.

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AltexSoft

AUGUST 25, 2021

And this technology of Natural Language Processing is available to all businesses. Keep reading to learn: What problems NLP can help solve. Available methods for text processing and which one to choose. What is Natural Language Processing? Here are some big text processing types and how they can be applied in real life.

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Knowledge Hut

MAY 3, 2024

If you are thinking of a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classifications as well as regression problems, K-Nearest Neighbors (K-NN) is a perfect choice. Learning K-Nearest Neighbors is a great way to introduce yourself to machine learning and classification in general.

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ProjectPro

JULY 23, 2021

Machine Learning and Deep Learning have experienced unusual tours from bust to boom from the last decade. But when it comes to large data sets, determining insights from them through deep learning algorithms and mining them becomes tricky. Image Source: [link] Nowadays, Deep Learning is almost everywhere.

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ProjectPro

APRIL 12, 2021

Deep learning job interviews. Most beginners in the industry break out in a cold sweat at the mere thought of a machine learning or a deep learning job interview. How do I prepare for my upcoming deep learning job interview? What kind of deep learning interview questions they are going to ask me?

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ProjectPro

FEBRUARY 16, 2023

At the core of such applications lies the science of machine learning, image processing, computer vision, and deep learning. As an example, consider the Facial Image Recognition System, it leverages the OpenCV Python library for implementing image processing techniques. What is OpenCV Python?

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Edureka

JUNE 7, 2024

Unlike traditional AI systems that operate on pre-existing data, generative AI models learn the underlying patterns and relationships within their training data and use that knowledge to create novel outputs that did not previously exist. This allows for a more controlled and step-by-step text generation process.

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Knowledge Hut

APRIL 26, 2024

Datasets play a crucial role and are at the heart of all Machine Learning models. Machine Learning without data sets will not exist because ML depends on data sets to bring out relevant insights and solve real-world problems. Quality data is therefore important to ensure the efficacy of a machine learning model.

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ProjectPro

APRIL 6, 2021

Machines learning algorithms on the other hand, while classifying images face these challenges, and Image Classification becomes an exciting problem for us to solve. Yann Le Cunn’s Convolutional Neural Network (something we’ll explore later in the article) still remains the backbone of computer vision algorithms.

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Knowledge Hut

JANUARY 18, 2024

and In my view, Data Science primarily focuses on engineering, processing, interpreting, and analyzing data to facilitate effective and informed decision-making. Machine Learning and Deep Learning are typically mentioned in conjunction with Artificial Intelligence which is generally considered sub-fields of Artificial Intelligence.

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Knowledge Hut

DECEMBER 29, 2023

In addition, there are professionals who want to remain current with the most recent capabilities, such as Machine Learning, Deep Learning, and Data Science, in order to further their careers or switch to an entirely other field. The Lambda design supports both batch processing and real-time operations.

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Knowledge Hut

MAY 2, 2024

It means a computer or a system designed with machine learning will identify, analyse and change accordingly and give the expected output when it comes across a new pattern of data, without any need of humans. Today, we all know that artificial neural networks play a key role in the thinking process of computers and machines.

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LinkedIn Engineering

JUNE 15, 2023

To remove this bottleneck, we built AvroTensorDataset , a TensorFlow dataset for reading, parsing, and processing Avro data. Today, we’re excited to open source this tool so that other Avro and Tensorflow users can use this dataset in their machine learning pipelines to get a large performance boost to their training workloads.

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ProjectPro

NOVEMBER 8, 2021

Whether it is quality control of crops through image classification or image processing for electronic deposits, computer vision techniques are transforming industries across the globe. The performance of computer vision algorithms has surpassed humans in specific tasks like detecting and labeling objects in terms of speed and accuracy.

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KDnuggets

FEBRUARY 21, 2022

A collection of cheat sheets that will help you prepare for a technical interview on Data Structures & Algorithms, Machine learning, Deep Learning, Natural Language Processing, Data Engineering, Web Frameworks.

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Knowledge Hut

DECEMBER 22, 2023

Data analytics, data mining, artificial intelligence, machine learning, deep learning, and other related matters are all included under the collective term "data science" When it comes to data science, it is one of the industries with the fastest growth in terms of income potential and career opportunities.

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RandomTrees

JUNE 17, 2024

This article looks into AI’s different uses in financial fraud detection, with a focus on techniques involving anomaly detection, machine learning algorithms, and real-time data analysis that help safeguard the credibility of financial systems. Machine Learning Algorithms Machine learning is very much important in fraud detection.

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Knowledge Hut

JULY 4, 2023

Pattern recognition is used in a wide variety of applications, including Image processing, Speech recognition, Biometrics, Medical diagnosis, and Fraud detection. It is a subfield of machine learning and artificial intelligence. Pattern recognition is a rapidly growing field with a wide range of applications.

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Knowledge Hut

MAY 2, 2024

Machine learning is a field that encompasses probability, statistics, computer science and algorithms that are used to create intelligent applications. Since machine learning is all about the study and use of algorithms, it is important that you have a base in mathematics. are the tools used in Inferential Statistics.

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Knowledge Hut

JULY 28, 2023

Data Labeling is the process of assigning meaningful tags or annotations to raw data, typically in the form of text, images, audio, or video. These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions. The more data we feed, the better the model gets.

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Knowledge Hut

MAY 30, 2024

Natural Language Processing Techniques 2. Evolutionary Algorithms and their Applications 9. Machine Learning Algorithms 5. Digital Image Processing: 6. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Artificial Intelligence (AI) 11.

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ProjectPro

JUNE 19, 2021

If you have heard of Machine Learning and Deep Learning, you must have also heard about cost (error or loss) functions. The cost function, in simple words, is a way of measuring the performance of a Machine Learning model by attributing a cost to every 'mistake' or wrong prediction that the model makes.

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Knowledge Hut

JULY 28, 2023

By learning from historical data, machine learning algorithms autonomously detect deviations, enabling timely risk mitigation. Machine learning offers scalability and efficiency, processing large datasets quickly. The following step is choosing a suitable machine learning method for anomaly detection.

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Knowledge Hut

DECEMBER 26, 2023

You can execute this by learning data science with python and working on real projects. These skills are essential to collect, clean, analyze, process and manage large amounts of data to find trends and patterns in the dataset. They identify gaps in their existing processes and leverage available data for the growth of the business.

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Knowledge Hut

MAY 31, 2023

Artificial intelligence (AI) projects are software-based initiatives that utilize machine learning, deep learning, natural language processing, computer vision, and other AI technologies to develop intelligent programs capable of performing various tasks with minimal human intervention. Let us get started!

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Edureka

SEPTEMBER 23, 2024

Let’s explore some of the most sought-after roles: AI/ML Engineer AI/ML engineers develop algorithms that enable machines to learn from data. They need a solid understanding of machine learning algorithms, deep learning, and programming languages like Python and R.

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Striim

JULY 18, 2024

An inference model is a form of machine learning model that leverages algorithms to analyze data. In the healthcare industry specifically, these models process real-time patient data to detect patterns, predict potential health issues, and suggest immediate care solutions to healthcare providers.

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Knowledge Hut

MAY 1, 2024

The art of analysing the data, extracting patterns, applying algorithms, tweaking the data to suit our requirements, and more – are all part s of data science. All these processes are done with the help of algorithms which are specially designed to perform a specific task. This is where Data Science comes into the picture.

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Tweag

JUNE 7, 2023

The availability of deep learning frameworks like PyTorch or JAX has revolutionized array processing, regardless of whether one is working on machine learning tasks or other numerical algorithms. However, writing high-performance array processing code in Haskell is still a non-trivial endeavor.

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