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Please ensure that any post tagged with #deeplearning is primarily on deep learning while generally can be on machine learning, data science, AI, CS, etc.

Please also be sure that your content adheres to the DEV Code of Conduct and that your comments are constructive and kind.

Conventional machine learning techniques are limited in their ability to automatically transform the raw unstructured data into a suitable internal representation. Deep learning methods are representation learning methods with multiple levels of representation, obtained by composing several simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level - to put it simply - Neural Networks. With the composition of enough such transformations, very complex functions can be learned - a universal approximator.
If you want to share something primarily on deep learning techniques please use #deeplearning tag.

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Gabby Six

Oct 6

Dust: The Radical Idea of Training AI Without Backpropagation

#ai #machinelearning #deeplearning #research

4 min read

cz

Oct 6

Understanding Prism: Dynamic Sparse Attention for Joint Video-Audio Generation

#ai #machinelearning #videogeneration #deeplearning

4 min read

Daniel Sam Pete Thiyagu

Oct 5

1,107 Tokens Per Second: The LLM That Doesn't Type

#ai #llm #machinelearning #deeplearning

5 min read

cognitalk

Oct 6

One Brain, Any Body: Google DeepMind's Keerthana on Gemini ER2 | 谷歌DeepMind Gemini Robotics 2 与通用机器人现状深度访谈

#ai #deeplearning #machinelearning #robotics

2 min read

Daniel Sam Pete Thiyagu

Oct 5

One Model, Three Jobs, Zero Retraining: The Spreadsheet Just Got Its Foundation Model

#ai #machinelearning #datascience #deeplearning

5 min read

Marita P

Oct 5

How AI Rap Duo Video Generators Work Under the Hood (and How to Pick One)

#ai #deeplearning #machinelearning

3 min read

Kathir

Oct 3

DAY 1 - GENERATIVE AI

#ai #deeplearning #llm #machinelearning

8 min read

Oct 3

RL 5: Learning Automata and stochastic environments (1961–1974)

#rl #machinelearning #deeplearning #ai

21 min read

Arun Kumar

Oct 3

Fine-tuning a 7B model needs 112 GB. The model is only 14 GB of it.

#machinelearning #llm #ai #deeplearning

3 min read

Oct 3

How AI Learns: Forward Pass and Loss Explained with 2 + 1

#ai #machinelearning #deeplearning #beginners

2 min read

Oct 1

Understanding Neural Networks: The Core Idea

#ai #deeplearning #machinelearning

11 min read

Prabhakar Chaudhary

Oct 1

UniEvo-VL: On-Policy Self-Distillation for Multimodal Image Generation

#ai #machinelearning #deeplearning #computervision

5 min read

Mira Ceti

Oct 1

RoPE vs sinusoidal positional encoding, measured: 55 logits of drift against 0.0005

#machinelearning #deeplearning #pytorch #python

7 min read

Sep 30

Why AI Lies Without Knowing It’s Lying | Victor Amit

#ai #machinelearning #llm #deeplearning

21 min read

Prabhakar Chaudhary

Sep 30

REAL-Q: How Dynamic Gradient Descent Fixes the Core Flaw in LLM Quantization

#machinelearning #deeplearning #llm #python

5 min read

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