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Neural Networks News
Medium
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Building Neural Networks Manually from Scratch: A Beginner’s Hello World
- The article explains how to build a neural network from scratch, step-by-step and without shortcuts.
- The process of training a neural network involves calculating errors and adjusting guesses based on those errors.
- A learning rate is used to control the size of adjustments, preventing overshoot and zigzagging.
- Activation functions are essential for neural networks, allowing them to model complex patterns.
- Bias is used to adjust the baseline of a network, enabling it to capture patterns that don't naturally pass through the origin.
- The article provides an example of building a neural network to approximate the function y = x + 1.
- The article explains forward and backpropagation, which respectively involve making predictions and learning from mistakes to adjust the network's parameters.
- The article also explains derivatives and the chain rule which are crucial to calculating gradients and improving a neural network's predictions.
- The code for a simple neural network is provided on GitHub for readers to experiment with.
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Medium
4w
380
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Combatting AI Improvement Slowdown, Part 7 — Hybrid Modeling Approaches
- This article discusses hybrid modeling approaches which bridge the gap between logical reasoning and data-driven learning using symbolic AI and neural networks. These hybrid systems provide interpretability through clear and logical rule-based reasoning, and at the same time learn and generalize from large datasets without requiring explicit rules. Hybrid modeling is beneficial in domains that demand both logical reasoning and robust pattern recognition, such as healthcare, finance, and autonomous systems. However, integrating multiple models presents several challenges, including integration complexity, scalability, and performance optimization. Ensemble techniques are used for improving model accuracy, and model evaluation methods such as task-specific metrics and pipeline integration are recommended. Finally, the article discusses balancing training signals, data representation gap, and increased model complexity as challenges that need to be addressed in hybrid modeling.
- Symbolic AI systems provide explicit rules and explanations for decisions, making them effective for tasks involving structured data, formal logic, or domain-specific expertise. Neural networks, on the other hand, are adept at handling unstructured data, such as images, audio, and natural language.
- Hybrid models are especially beneficial in fields where interpretability, compliance, and logical consistency are critical, such as regulated industries or dynamic systems. Hybrid ensembles are particularly useful in scenarios like complex decision-making, imbalanced data, multi-modal inputs, and high-stakes applications.
- Joint optimization is used to ensure that neural and symbolic components are trained simultaneously allowing gradients from neural learning to inform symbolic rule refinement. The article recommends using modular architectures that decouple neural and symbolic components, allowing independent scaling. Debugging mechanisms like logging and testing, and explainability frameworks like LIME or SHAP are suggested for debugging complex hybrid models.
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Medium
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35
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Deep Learning at it’s Core
- Deep learning is a subset of machine learning that uses artificial neural networks (ANNs) to process large volumes of data and extract high-level patterns.
- Deep neural networks are able to handle highly complex tasks such as image recognition, natural language processing, and voice recognition with remarkable accuracy.
- Deep learning has become a revolutionary technology in fields such as AI, robotics, healthcare, autonomous driving, and more.
- Deep learning operates on neural networks composed of layers of interconnected nodes that work together to process information.
- Backpropagation is the process of updating the weights in a neural network to minimize errors in predictions.
- To improve the performance of deep learning models, optimization algorithms such as Stochastic Gradient Descent (SGD) or Adam are used.
- There are several types of deep learning models, each tailored to solve specific types of problems, such as Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, and Generative Adversarial Networks.
- Deep learning has been widely adopted across various industries such as healthcare, autonomous vehicles, finance and trading, natural language processing, and retail and e-commerce.
- Deep learning models offer many advantages over traditional machine learning techniques, including high accuracy, automatic feature extraction, scalability, and real-time performance.
- As the field of deep learning continues to evolve, some areas to watch for include explainability and interpretability, multimodal learning, and AI ethics.
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Medium
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Designing Convolution Network Architectures
- In this post we explore the AnyNet design space, focusing on efficient principles, scalable strategies, and challenges in hyperparameter optimization.
- Identifying optimal hyperparameters in the AnyNet design space is computationally infeasible.
- The AnyNetX E design space consists of simple networks following easy-to-interpret design principles.
- By adapting the number of stages, channels, and depth, you can tune the network for various tasks.
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Medium
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Predicting Stock Prices with Neural Networks
- When predicting stock prices, there are specific trends and indicators such as stock price trends, technical indicators, market sentiment, seasonality and events, and volatility that are significant to analyze.
- Machine learning and deep learning models have opened new opportunities for making more informed predictions in the stock market.
- Long Short-Term Memory (LSTM) networks have shown promising results in modeling the patterns in stock market data.
- LSTMs consist of units called memory cells, each containing three gates (forget gate, input gate, and output gate) that work together to regulate the flow of information, enabling LSTMs to retain or forget data as needed.
- LSTMs excel at identifying patterns in data with temporal dependencies, making them well-suited for time series tasks like stock price prediction.
- To implement the LSTM model, we need to import the necessary libraries, download stock data from Yahoo Finance, apply MinMax scaling to the data, and create X and y values using a function called prepare_data.
- We then create a standard sequential neural network that takes one feature per timestep, with two LSTM layers, and a Dense layer to produce a single scalar value, and use the Adam optimizer to minimize loss during training.
- After training the model, we can use it to make predictions on our testing data, which shows that the predicted prices are very close to the actual prices.
- However, there are limitations when predicting future data, as the model would need to predict a certain day, remove the first element of the input values, and add the new prediction to the input values to predict the next, leading to an increasing pattern that follows a straight line.
- Overall, the use of LSTMs in stock market prediction has demonstrated success and has the potential to inform investment decisions and improve financial outcomes.
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Hackernoon
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How Hyperparameter Tuning Enhances Anchor Data Augmentation for Robust Regression
- The paper discusses how hyperparameter tuning enhances Anchor Data Augmentation (ADA) for robust regression.
- Anchor Data Augmentation is a technique that introduces artificial heterogeneity in the target variable by leveraging an anchor variable.
- The paper presents the construction of the anchor matrix and the impact of hyperparameter values on the augmentation procedure.
- The authors recommend applying ADA on each minibatch instead of the entire dataset to add diversity to the mixing behavior.
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Hackernoon
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ADA: A Powerful Data Augmentation Technique for Improved Regression Robustness
- The authors introduce Anchor Data Augmentation (ADA), an extension of Anchor Regression for the purpose of data augmentation.
- ADA is a novel causal approach to increase the robustness in regression problems.
- In ADA, we systematically mix multiple samples based on a collective similarity criterion, which is determined via clustering.
- Our empirical evaluations across diverse synthetic and real-world regression problems consistently demonstrate the effectiveness of ADA, especially for limited data availability.
- ADA is competitive with or outperforms state-of-the-art data augmentation strategies for regression problems.
- The authors believe that ADA can be applied to any regression setting, and they have not found any case in which the results were detrimental.
- It is important to note that the choice and combination of the data augmentation technique depends on the specific problem and using the wrong augmentation method may introduce additional bias to the model.
- Detecting emerging problems due to data augmentation may not be straightforward, and the model's predictions should be used with caution on new data that reflects the potential distribution shifts or variations encountered in real-world.
- The purpose of data augmentation is to compensate for data scarcity in multiple domains where gathering and labeling data accurately by experts is impractical, expensive, or time-consuming.
- If applied properly, it can effectively expand the training dataset, reduce overfitting, and improve the model's robustness, as was shown in the paper.
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Hackernoon
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ADA's Impact on Out-of-Distribution Robustness
- ADA (Anchor Data Augmentation) is evaluated and compared to prior approaches on tasks involving out-of-distribution robustness.
- Four out-of-distribution datasets are used, including RCFashionMNIST, Communities and Crime, SkillCraft1 Master Table, and Drug-target Interactions.
- Results show that ADA is competitive with C-Mixup and other data augmentation strategies, and significantly outperforms them on the SkillCraft dataset.
- ADA reduces the error by around 15% compared to the ERM solution on the SkillCraft data.
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Hackernoon
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ADA Outperforms ERM and Competes with C-Mixup in In-Distribution Generalization Tasks
- ADA (Anchor Data Augmentation) outperforms ERM (Expected Risk Minimization) and competes with C-Mixup in in-distribution generalization tasks.
- ADA is evaluated and compared to other approaches on tasks involving in-distribution generalization using various datasets.
- ADA consistently improves regression fit compared to ERM and is superior to other data augmentation strategies.
- ADA shows significant improvement in reducing error for the Airfoil dataset.
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9 Likes
Hackernoon
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ADA vs C-Mixup: Performance on California and Boston Housing Datasets
- ADA (Anchor Data Augmentation) is compared to C-Mixup for data augmentation on the California and Boston housing datasets.
- ADA is shown to be superior to C-Mixup in the experimental setup for data augmentation.
- The experiments involve using ridge regression and MLP models with varying number of hidden units.
- Performance of ADA and C-Mixup approaches are compared, with ADA showing better performance in low training sample scenarios.
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Hackernoon
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Evaluating ADA: Experimental Results on Linear and Housing Datasets
- The experimental results on linear and housing datasets evaluate the performance of ADA (Anchor Data Augmentation).
- In the in-distribution setting for a linear regression problem, ADA shows improved performance in the low data regime.
- ADA and C-Mixup are applied to the California and Boston Housing datasets, analyzing the impact on model performance as the number of training samples increases.
- ADA and C-Mixup provide gains in performance even in cases where the number of training examples is not sufficient to achieve the error floor.
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Hackernoon
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How to Implement ADA for Data Augmentation in Nonlinear Regression Models
- The paper discusses the implementation of Anchored Data Augmentation (ADA) for data augmentation in nonlinear regression models.
- The authors propose ADA as a method to generate minibatches of data for training neural networks or any other nonlinear regressor using stochastic gradient descent.
- The ADA algorithm is presented step by step, and it involves repeating the augmentation with different parameter combinations for each minibatch.
- The paper concludes by highlighting the availability of the paper on arXiv under CC0 1.0 DEED license.
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Hackernoon
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Anchor Data Augmentation (ADA): A Domain-Agnostic Method for Enhancing Regression Models
- Anchor Data Augmentation (ADA) is a domain-agnostic method for enhancing regression models.
- ADA is inspired by Anchor Regression (AR) and does not require previous knowledge about data invariances or manually engineered transformations.
- Unlike existing domain-agnostic data augmentation methods, ADA does not require training of an expensive generative model.
- ADA can be readily applied to regression problems and its effect on performance remains minimal.
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8 Likes
Hackernoon
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301
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Anchor Regression: The Secret to Stable Predictions Across Shifting Data
- Anchor Regression proposes a method to enforce stability and robustness in predictive models.
- The method relaxes the regularization in the optimization problem and interpolates between partialling out and instrumental variable estimation.
- The solution of Anchor Regression optimizes worst-case risk under shift interventions on anchors.
- The method increases the robustness of predictions to distribution shifts at the cost of reducing in-distribution generalization.
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18 Likes
Hackernoon
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A Comprehensive Overview of Data Augmentation Methods
- Data augmentation methods have been proposed for various applications.
- Different approaches, such as human-designed transformations and policy/search-based automated methods, have been used.
- Generic transformations like Gaussian or adversarial noise, dropout, and generative models have also been effective.
- Mixup, a popular data augmentation technique, has been extended for classification tasks but may adversely impact regression predictions.
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