𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: 𝐀 𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞 𝐨𝐟 𝐊𝐞𝐲 𝐌𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞𝐬

Deep learning has evolved over several decades through continuous advances in neural network models, learning algorithms, and computational power.
The timeline below highlights the key milestones and contributors that shaped modern deep learning.

1943 -- 𝐍𝐞𝐮𝐫𝐚𝐥 𝐌𝐨𝐝𝐞𝐥 (𝐌𝐜𝐂𝐮𝐥𝐥𝐨𝐜𝐡 & 𝐏𝐢𝐭𝐭𝐬)
• First mathematical model of a biological neuron
• Foundation of artificial neural networks

1957 -- 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐫𝐨𝐧 (𝐅𝐫𝐚𝐧𝐤 𝐑𝐨𝐬𝐞𝐧𝐛𝐥𝐚𝐭𝐭)
• First learning algorithm for neural networks
• Enabled binary classification using weighted inputs

1982 -- 𝐇𝐨𝐩𝐟𝐢𝐞𝐥𝐝 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 (𝐉𝐨𝐡𝐧 𝐇𝐨𝐩𝐟𝐢𝐞𝐥𝐝)
• Introduced recurrent neural networks
• Enabled associative memory

1985 -- 𝐁𝐨𝐥𝐭𝐳𝐦𝐚𝐧𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 (𝐇𝐢𝐧𝐭𝐨𝐧 & 𝐒𝐞𝐣𝐧𝐨𝐰𝐬𝐤𝐢)
• Introduced stochastic learning
• Basis for deep representations

1986 -- 𝐁𝐚𝐜𝐤𝐩𝐫𝐨𝐩𝐚𝐠𝐚𝐭𝐢𝐨𝐧 (𝐑𝐮𝐦𝐞𝐥𝐡𝐚𝐫𝐭, 𝐇𝐢𝐧𝐭𝐨𝐧, 𝐖𝐢𝐥𝐥𝐢𝐚𝐦𝐬)
• Enabled training of multilayer networks
• Core optimization algorithm

𝐋𝐚𝐭𝐞 1980𝐬 -1990𝐬 -- 𝐀𝐈 𝐖𝐢𝐧𝐭𝐞𝐫
• Limited computation and reduced funding
• Shift toward simpler ML models

1990 -- 𝐒𝐕𝐌 (𝐕𝐥𝐚𝐝𝐢𝐦𝐢𝐫 𝐕𝐚𝐩𝐧𝐢𝐤)
• Margin-based classification
• Effective for high-dimensional data

1997 -- 𝐋𝐒𝐓𝐌 (𝐇𝐨𝐜𝐡𝐫𝐞𝐢𝐭𝐞𝐫 & 𝐒𝐜𝐡𝐦𝐢𝐝𝐡𝐮𝐛𝐞𝐫)
• Solved vanishing gradient problem
• Enabled sequence learning

𝐋𝐚𝐭𝐞 1990𝐬 - 2000𝐬 -- 𝐆𝐏𝐔 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠
• Accelerated neural network training
• Enabled large-scale deep learning

2012 -- 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 (𝐀𝐥𝐞𝐱𝐍𝐞𝐭)
• CNN breakthrough on ImageNet
• Widespread DL adoption

2014 -- 𝐆𝐀𝐍𝐬 (𝐈𝐚𝐧 𝐆𝐨𝐨𝐝𝐟𝐞𝐥𝐥𝐨𝐰)
• Introduced adversarial learning
• Enabled data and image generation

2016 -- 𝐃𝐞𝐞𝐩 𝐑𝐋 (𝐀𝐥𝐩𝐡𝐚𝐆𝐨)
• DL + reinforcement learning
• Superhuman decision-making

2017 -- 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 (𝐀𝐬𝐡𝐢𝐬𝐡 𝐕𝐚𝐬𝐰𝐚𝐧𝐢 𝐞𝐭 𝐚𝐥.)
• Introduced self-attention
• Foundation of modern NLP

2020 -- 𝐒𝐞𝐥𝐟-𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐘𝐚𝐧𝐧 𝐋𝐞𝐂𝐮𝐧)
• Reduced need for labeled data
• Improved representation learning

2022 - 𝐏𝐫𝐞𝐬𝐞𝐧𝐭 -- 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬
• Scaled transformers for multimodal AI
• Real-world deployment at scale