Best of Neural Networks — January 2025

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·1y

   Loss Function of 16 ML Algos

   Provides a visual summary of loss functions used in 16 common machine learning algorithms. It highlights the importance of selecting appropriate loss functions for different tasks. Covers algorithms like linear regression, logistic regression, decision trees, SVMs, neural networks, and various boosting methods. Additional resources and readings are suggested to enhance understanding and application in real-world scenarios.

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2. 2
   Video

   YouTube·1y

   why ai neural networks will change trading forever and how to build yours in minutes!

   AI and neural networks are revolutionizing trading by developing strategies that can outperform traditional methods like the S&P 500 Buy and Hold. There are different types of neural networks including feed forward and recurrent neural networks, each with unique advantages. Recurrent neural networks, for example, can remember past data, making them ideal for predicting stock prices. Despite their computational complexity, platforms like QuantConnect provide tools to build and test these models. Practical examples and comprehensive courses are available to help individuals understand and implement these advanced strategies.

   19
3. 3
   Article

   Medium·1y

   Google Titans: End of Transformer based LLMs?

   Google's Titans introduce a new family of neural architectures designed to address the limitations of Transformers, particularly in handling long-term dependencies and large context windows. Key innovations include a neural long-term memory module inspired by human memory systems, which can memorize historical context effectively. Titans offer three variants for memory integration and combine short-term attention with long-term memory, allowing better performance on tasks requiring deep context understanding like language modeling and time series forecasting.

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