Latest deep learning technology and object recognition

Learn how to understand advanced machine learning models without math and implement them easily in Python.

Beginner

Machine Learning(ML), Python, Scikit-Learn

[Python] Easily understand and implement machine learning without math

dlbro

$51.70

Beginner / Machine Learning(ML), Python, Scikit-Learn, Big Data

4.7

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From the concept of the latest deep learning technology Vision Transformer to Pytorch implementation

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$66.00

Intermediate / Vision Transformer, Deep Learning(DL), PyTorch, Computer Vision(CV)

4.8

(97)

This is a lecture that studies Vision Transformer, one of the latest deep learning technologies, and implements a paper using Pytorch. Come experience the new future of the vision field with me!

Intermediate

Vision Transformer, Deep Learning(DL), PyTorch

From the concept of the latest deep learning technology Vision Transformer to Pytorch implementation

dlbro

$66.00

Intermediate / Vision Transformer, Deep Learning(DL), PyTorch, Computer Vision(CV)

4.8

(97)

Linear Algebra Leading to Machine Learning/Deep Learning

dlbro

$42.90

Beginner / Linear Algebra, Machine Learning(ML), Deep Learning(DL)

4.5

(46)

600+

Covers essential linear algebra for Machine Learning/Deep Learning research.

Beginner

Linear Algebra, Machine Learning(ML), Deep Learning(DL)

Linear Algebra Leading to Machine Learning/Deep Learning

dlbro

$42.90

Beginner / Linear Algebra, Machine Learning(ML), Deep Learning(DL)

4.5

(46)

600+

[PyTorch] Deep Learning Leading to Practical AI - From Basics to Paper Implementation

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$57.20

Basic / Deep Learning(DL), Python, PyTorch, Artificial Neural Network, Machine Learning(ML)

4.6

(82)

1,400+

This is a lecture on implementing various artificial neural networks using PyTorch, a deep learning framework with very high utility in the field of artificial intelligence.

Basic

Deep Learning(DL), Python, PyTorch

[PyTorch] Deep Learning Leading to Practical AI - From Basics to Paper Implementation

dlbro

$57.20

Basic / Deep Learning(DL), Python, PyTorch, Artificial Neural Network, Machine Learning(ML)

4.6

(82)

1,400+

Understanding the concept of deep learning leading to practical artificial intelligence

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$55.00

Beginner / Deep Learning(DL), Artificial Neural Network, Machine Learning(ML)

4.7

(108)

1,300+

This course provides essential knowledge needed to understand the structure and operating principles of various artificial neural networks and create good models.

Beginner

Deep Learning(DL), Artificial Neural Network, Machine Learning(ML)

Understanding the concept of deep learning leading to practical artificial intelligence

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$55.00

Beginner / Deep Learning(DL), Artificial Neural Network, Machine Learning(ML)

4.7

(108)

1,300+

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Shin Kyung-shik's Deep Learning Odyssey - Image Basics for Computer Vision

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Beginner / Computer Vision(CV), Deep Learning(DL), AI

Build a solid theoretical foundation of images essential before learning deep learning-based computer vision. Understand the essence of image data, such as pixels, resolution, and color representation, and cultivate practical programming skills to read, transform, and preprocess images using Python.

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Deep Learning and PyTorch Bootcamp for Beginners (Easy! From Basics to ChatGPT's Core Transformer) [Data Analysis/Science Part 3]

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This is a newly designed course that allows you to gradually learn the mathematics, theory, PyTorch-based implementation, transfer learning, and GPT's core transformer needed to understand deep learning, based on the instructor's own failed experiences when first learning deep learning.

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AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

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This course allows you to build an AI portfolio by cloning the Amenity Detection project from the Airbnb Data Science team—a prime example of TensorFlow utilization—and documenting the entire process on a GitHub blog.

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