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𝗛𝗼𝘄 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗪𝗼𝗿𝗸 A Convolutional Neural Network (CNN) works by using layers to process images: • Convolutional layers scan the input image using filters to detect features like edges, textures, and patterns.👇

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰

12,929 subscribers

41,791 просмотров • 1 год назад •via X (Twitter)

Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 6

Фото профиля ramakrushna — e/acc

ramakrushna — e/acc1 год назад

• Next, Pooling layers reduce the spatial dimensions while preserving important information. • Then, fully connected layers take these extracted features and make final classifications by weighting connections between all neurons.

Фото профиля Rainmaker

Rainmaker2 лет назад

🚀 Discover how Reinforcement Learning can transform trading strategies! Check out my free Substack for full code and backtest of the completed Q-learning algorithm. Don't miss out! 📈✨

Фото профиля 爆死の神

爆死の神1 год назад

What is this

Фото профиля Kit

Kit1 год назад

This thing is smarter than me

Фото профиля ramakrushna — e/acc

ramakrushna — e/acc1 год назад

😬😇

Фото профиля Sandro Macena

Sandro Macena1 год назад

For what is it for? For wich porpouse?

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Fukushima's video (1986) shows a CNN that recognises handwritten digits [3], three years before LeCun's video (1989). CNN timeline taken from [5]: ★ 1969: Kunihiko Fukushima published rectified linear units or ReLUs [1] which are now extensively used in CNNs. ★ 1979: Fukushima published the basic CNN architecture with convolution layers and downsampling layers [2]. He called it neocognitron. It was trained by unsupervised learning rules. Compute was 100 times more expensive than in 1989, and a billion times more expensive than today. ★ 1986: Fukushima's video on recognising hand-written digits [3]. ★ 1988: Wei Zhang et al had the first "modern" 2-dimensional CNN trained by backpropagation, and also applied it to character recognition [4]. Compute was about 10 million times more expensive than today. ★ 1989-: later work by others [5]. REFERENCES (more in [5]) [1] K. Fukushima (1969). Visual feature extraction by a multilayered network of analog threshold elements. IEEE Transactions on Systems Science and Cybernetics. 5 (4): 322-333. This work introduced rectified linear units or ReLUs, now widely used in CNNs and other neural nets. [2] K. Fukushima (1979). Neural network model for a mechanism of pattern recognition unaffected by shift in position—Neocognitron. Trans. IECE, vol. J62-A, no. 10, pp. 658-665, 1979. The first deep convolutional neural network architecture, with alternating convolutional layers and downsampling layers. In Japanese. English version: 1980. [3] Movie produced by K. Fukushima, S. Miyake and T. Ito (NHK Science and Technical Research Laboratories), in 1986. YouTube: [4] W. Zhang, J. Tanida, K. Itoh, Y. Ichioka. Shift-invariant pattern recognition neural network and its optical architecture. Proc. Annual Conference of the Japan Society of Applied Physics, 1988. First "modern" backpropagation-trained 2-dimensional CNN, applied to character recognition. [5] J. Schmidhuber (AI Blog, 2025). Who invented convolutional neural networks?

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