# Deep Learning Methodologies ⎊ Area ⎊ Resource 1 --- ## What is the Algorithm of Deep Learning Methodologies? Deep learning methodologies increasingly inform quantitative models within cryptocurrency, options, and derivatives markets, moving beyond traditional statistical approaches. These algorithms, often employing recurrent neural networks (RNNs) or transformers, excel at capturing complex, non-linear dependencies inherent in high-frequency data and order book dynamics. Specifically, reinforcement learning techniques are being explored for automated trading strategy optimization, adapting to evolving market conditions and minimizing transaction costs. The efficacy of these approaches hinges on robust feature engineering and careful consideration of overfitting, particularly given the limited historical data available for some crypto assets. ## What is the Analysis of Deep Learning Methodologies? Sophisticated analysis leveraging deep learning provides enhanced insights into market microstructure and price discovery processes. Techniques like convolutional neural networks (CNNs) can identify subtle patterns in historical price charts and order flow data, potentially predicting short-term price movements or identifying anomalous trading behavior. Furthermore, deep learning facilitates sentiment analysis from social media and news sources, offering a complementary perspective to traditional technical indicators. Such analysis requires substantial computational resources and rigorous backtesting to validate model performance and assess robustness across different market regimes. ## What is the Architecture of Deep Learning Methodologies? The architectural design of deep learning models is crucial for their successful application in financial derivatives. Hybrid architectures, combining CNNs for pattern recognition with RNNs for time-series analysis, are common for forecasting volatility or option prices. Attention mechanisms, integral to transformer models, allow the network to focus on the most relevant data points, improving predictive accuracy. Scalability is a key consideration, necessitating distributed training frameworks and optimized hardware to handle the computational demands of large datasets and complex models. --- ## [Machine Learning](https://term.greeks.live/term/machine-learning/) Meaning ⎊ Machine Learning provides adaptive models for processing high-velocity, non-linear crypto data, enhancing volatility prediction and risk management in decentralized derivatives. ⎊ Term ## [Machine Learning Models](https://term.greeks.live/definition/machine-learning-models/) Computational algorithms that learn from data to make predictions or decisions. ⎊ Term ## [Machine Learning Risk Models](https://term.greeks.live/term/machine-learning-risk-models/) Meaning ⎊ Machine learning risk models provide a necessary evolution from traditional quantitative methods by quantifying and predicting risk factors invisible to legacy frameworks. ⎊ Term ## [Stress Testing Methodologies](https://term.greeks.live/definition/stress-testing-methodologies/) Analytical procedures that subject protocols to extreme, hypothetical market scenarios to assess resilience and solvency. ⎊ Term ## [Risk Assessment Methodologies](https://term.greeks.live/term/risk-assessment-methodologies/) Meaning ⎊ Risk assessment for decentralized options requires a multi-vector framework that integrates market risk, smart contract integrity, oracle reliability, and systemic liquidity dynamics. ⎊ Term ## [Deep Learning for Order Flow](https://term.greeks.live/term/deep-learning-for-order-flow/) Meaning ⎊ Deep learning for order flow analyzes high-frequency market data to predict short-term price movements and optimize execution strategies in complex, adversarial crypto environments. ⎊ Term ## [Machine Learning Risk Analytics](https://term.greeks.live/term/machine-learning-risk-analytics/) Meaning ⎊ Machine Learning Risk Analytics provides dynamic, data-driven risk modeling essential for managing non-linear volatility and systemic risk in crypto options. ⎊ Term ## [Machine Learning Algorithms](https://term.greeks.live/term/machine-learning-algorithms/) Meaning ⎊ Machine learning algorithms process non-stationary crypto market data to provide dynamic risk management and pricing for decentralized options. ⎊ Term ## [Data Aggregation Methodologies](https://term.greeks.live/definition/data-aggregation-methodologies/) Statistical techniques for combining multiple price sources into a single, reliable value while filtering out market noise. ⎊ Term ## [Adversarial Machine Learning Scenarios](https://term.greeks.live/term/adversarial-machine-learning-scenarios/) Meaning ⎊ Adversarial machine learning scenarios exploit vulnerabilities in financial models by manipulating data inputs, leading to mispricing or incorrect liquidations in crypto options protocols. ⎊ Term ## [Adversarial Machine Learning](https://term.greeks.live/term/adversarial-machine-learning/) Meaning ⎊ Adversarial machine learning in crypto options involves exploiting automated financial models to create arbitrage opportunities or trigger systemic liquidations. ⎊ Term ## [Machine Learning Forecasting](https://term.greeks.live/term/machine-learning-forecasting/) Meaning ⎊ Machine learning forecasting optimizes crypto options pricing by modeling non-linear volatility dynamics and systemic risk using on-chain data and market microstructure analysis. ⎊ Term ## [Machine Learning Volatility Forecasting](https://term.greeks.live/term/machine-learning-volatility-forecasting/) Meaning ⎊ Machine learning volatility forecasting adapts predictive models to crypto's unique non-linear dynamics for precise options pricing and risk management. ⎊ Term ## [Zero-Knowledge Machine Learning](https://term.greeks.live/term/zero-knowledge-machine-learning/) Meaning ⎊ Zero-Knowledge Machine Learning secures computational integrity for private, off-chain model inference within decentralized derivative settlement layers. ⎊ Term ## [Order Book Pattern Detection Software and Methodologies](https://term.greeks.live/term/order-book-pattern-detection-software-and-methodologies/) Meaning ⎊ Order Book Pattern Detection is the critical algorithmic framework for predicting short-term volatility and liquidity events in crypto options by analyzing microstructural order flow. ⎊ Term ## [Order Book Pattern Detection Methodologies](https://term.greeks.live/term/order-book-pattern-detection-methodologies/) Meaning ⎊ Order Book Pattern Detection Methodologies identify structural intent and liquidity shifts to reveal the hidden mechanics of price discovery. ⎊ Term ## [Margin Calculation Methodologies](https://term.greeks.live/term/margin-calculation-methodologies/) Meaning ⎊ Margin calculation methodologies serve as the mathematical foundation for systemic solvency by quantifying risk and enforcing collateral requirements in real-time. ⎊ Term ## [Network Security Testing Methodologies](https://term.greeks.live/term/network-security-testing-methodologies/) Meaning ⎊ Network security testing methodologies provide the essential adversarial validation required to ensure the stability of decentralized financial derivatives. ⎊ Term ## [Deep in the Money](https://term.greeks.live/definition/deep-in-the-money/) A state where an option has high intrinsic value and low extrinsic value, causing it to mimic the underlying asset. ⎊ Term ## [Machine Learning Applications](https://term.greeks.live/term/machine-learning-applications/) Meaning ⎊ Machine learning applications automate complex derivative pricing and risk management by identifying predictive patterns in decentralized market data. ⎊ Term ## [Deep Learning Option Pricing](https://term.greeks.live/term/deep-learning-option-pricing/) Meaning ⎊ Deep Learning Option Pricing replaces static formulas with adaptive neural models to improve derivative valuation in high-volatility decentralized markets. ⎊ Term ## [Backtesting Methodologies](https://term.greeks.live/definition/backtesting-methodologies/) Using historical data to simulate and validate trading strategies to assess their performance and risk before live deployment. ⎊ Term ## [Deep Learning Models](https://term.greeks.live/term/deep-learning-models/) Meaning ⎊ Deep Learning Models provide dynamic, non-linear frameworks for pricing crypto options and managing risk within decentralized market structures. ⎊ Term ## [Deep Out-of-the-Money Options](https://term.greeks.live/definition/deep-out-of-the-money-options/) Low-cost derivative contracts used as insurance against extreme price movements due to their distance from market price. ⎊ Term ## [Off-Chain Machine Learning](https://term.greeks.live/term/off-chain-machine-learning/) Meaning ⎊ Off-Chain Machine Learning optimizes decentralized derivative markets by delegating complex computations to scalable layers while ensuring cryptographic trust. ⎊ Term ## [Penetration Testing Methodologies](https://term.greeks.live/term/penetration-testing-methodologies/) Meaning ⎊ Penetration testing methodologies provide the essential mathematical and structural verification required to maintain solvency in decentralized derivatives. ⎊ Term ## [Machine Learning Finance](https://term.greeks.live/term/machine-learning-finance/) Meaning ⎊ Machine Learning Finance enables autonomous, adaptive risk management and optimized pricing within decentralized derivatives markets. ⎊ Term ## [Blockchain Network Security Methodologies](https://term.greeks.live/term/blockchain-network-security-methodologies/) Meaning ⎊ Blockchain Network Security Methodologies provide the cryptographic and economic foundation necessary for trustless, irreversible financial settlement. ⎊ Term ## [Protocol Security Testing Methodologies](https://term.greeks.live/term/protocol-security-testing-methodologies/) Meaning ⎊ Protocol security testing methodologies provide the essential frameworks to verify code integrity and economic resilience in decentralized finance. ⎊ Term ## [Machine Learning Security](https://term.greeks.live/term/machine-learning-security/) Meaning ⎊ Machine Learning Security protects decentralized financial protocols by ensuring the integrity of algorithmic inputs against adversarial manipulation. ⎊ Term --- ## Raw Schema Data ```json { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://term.greeks.live/" }, { "@type": "ListItem", "position": 2, "name": "Area", "item": "https://term.greeks.live/area/" }, { "@type": "ListItem", "position": 3, "name": "Deep Learning Methodologies", "item": "https://term.greeks.live/area/deep-learning-methodologies/" }, { "@type": "ListItem", "position": 4, "name": "Resource 1", "item": "https://term.greeks.live/area/deep-learning-methodologies/resource/1/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "What is the Algorithm of Deep Learning Methodologies?", "acceptedAnswer": { "@type": "Answer", "text": "Deep learning methodologies increasingly inform quantitative models within cryptocurrency, options, and derivatives markets, moving beyond traditional statistical approaches. 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The layers are nested within each other, forming a dynamic, spiraling pattern around a central opening against a dark background." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/margin-calculation-methodologies/", "url": "https://term.greeks.live/term/margin-calculation-methodologies/", "headline": "Margin Calculation Methodologies", "description": "Meaning ⎊ Margin calculation methodologies serve as the mathematical foundation for systemic solvency by quantifying risk and enforcing collateral requirements in real-time. ⎊ Term", "datePublished": "2026-03-05T18:07:29+00:00", "dateModified": "2026-03-05T18:08:33+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/algorithmic-execution-of-smart-contract-logic-in-decentralized-finance-liquidation-protocols.jpg", "width": 3850, "height": 2166, "caption": "A high-tech stylized visualization of a mechanical interaction features a dark, ribbed screw-like shaft meshing with a central block. A bright green light illuminates the precise point where the shaft, block, and a vertical rod converge." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/network-security-testing-methodologies/", "url": "https://term.greeks.live/term/network-security-testing-methodologies/", "headline": "Network Security Testing Methodologies", "description": "Meaning ⎊ Network security testing methodologies provide the essential adversarial validation required to ensure the stability of decentralized financial derivatives. ⎊ Term", "datePublished": "2026-03-09T13:04:59+00:00", "dateModified": "2026-03-09T13:08:53+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/algorithmic-blockchain-protocol-architecture-illustrating-cryptographic-primitives-and-network-consensus-mechanisms.jpg", "width": 3850, "height": 2166, "caption": "A close-up view of a stylized, futuristic double helix structure composed of blue and green twisting forms. Glowing green data nodes are visible within the core, connecting the two primary strands against a dark background." } }, { "@type": "Article", "@id": "https://term.greeks.live/definition/deep-in-the-money/", "url": "https://term.greeks.live/definition/deep-in-the-money/", "headline": "Deep in the Money", "description": "A state where an option has high intrinsic value and low extrinsic value, causing it to mimic the underlying asset. ⎊ Term", "datePublished": "2026-03-09T13:59:28+00:00", "dateModified": "2026-04-19T10:55:57+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/decentralized-finance-protocol-mechanics-illustrating-automated-market-maker-liquidity-and-perpetual-funding-rate-calculation.jpg", "width": 3850, "height": 2166, "caption": "A close-up view presents an abstract mechanical device featuring interconnected circular components in deep blue and dark gray tones. A vivid green light traces a path along the central component and an outer ring, suggesting active operation or data transmission within the system." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/machine-learning-applications/", "url": "https://term.greeks.live/term/machine-learning-applications/", "headline": "Machine Learning Applications", "description": "Meaning ⎊ Machine learning applications automate complex derivative pricing and risk management by identifying predictive patterns in decentralized market data. ⎊ Term", "datePublished": "2026-03-09T20:03:09+00:00", "dateModified": "2026-03-09T20:03:40+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/algorithmic-volatility-arbitrage-processing-within-decentralized-finance-structured-product-protocols.jpg", "width": 3850, "height": 2166, "caption": "A close-up view presents a futuristic, dark-colored object featuring a prominent bright green circular aperture. Within the aperture, numerous thin, dark blades radiate from a central light-colored hub." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/deep-learning-option-pricing/", "url": "https://term.greeks.live/term/deep-learning-option-pricing/", "headline": "Deep Learning Option Pricing", "description": "Meaning ⎊ Deep Learning Option Pricing replaces static formulas with adaptive neural models to improve derivative valuation in high-volatility decentralized markets. ⎊ Term", "datePublished": "2026-03-10T15:51:11+00:00", "dateModified": "2026-03-10T15:51:39+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/algorithmic-risk-management-engine-for-defi-derivatives-options-pricing-and-smart-contract-composability.jpg", "width": 3850, "height": 2166, "caption": "The image displays a close-up render of an advanced, multi-part mechanism, featuring deep blue, cream, and green components interlocked around a central structure with a glowing green core. The design elements suggest high-precision engineering and fluid movement between parts." } }, { "@type": "Article", "@id": "https://term.greeks.live/definition/backtesting-methodologies/", "url": "https://term.greeks.live/definition/backtesting-methodologies/", "headline": "Backtesting Methodologies", "description": "Using historical data to simulate and validate trading strategies to assess their performance and risk before live deployment. ⎊ Term", "datePublished": "2026-03-10T16:27:22+00:00", "dateModified": "2026-05-28T18:51:51+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/layered-defi-protocol-architecture-supporting-options-chains-and-risk-stratification-analysis.jpg", "width": 3850, "height": 2166, "caption": "This abstract 3D render displays a complex structure composed of navy blue layers, accented with bright blue and vibrant green rings. The form features smooth, off-white spherical protrusions embedded in deep, concentric sockets." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/deep-learning-models/", "url": "https://term.greeks.live/term/deep-learning-models/", "headline": "Deep Learning Models", "description": "Meaning ⎊ Deep Learning Models provide dynamic, non-linear frameworks for pricing crypto options and managing risk within decentralized market structures. ⎊ Term", "datePublished": "2026-03-10T19:18:05+00:00", "dateModified": "2026-03-10T19:18:32+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/volatility-and-risk-aggregation-in-financial-derivatives-visualizing-layered-synthetic-assets-and-market-depth.jpg", "width": 3850, "height": 2166, "caption": "This abstract 3D form features a continuous, multi-colored spiraling structure. The form's surface has a glossy, fluid texture, with bands of deep blue, light blue, white, and green converging towards a central point against a dark background." } }, { "@type": "Article", "@id": "https://term.greeks.live/definition/deep-out-of-the-money-options/", "url": "https://term.greeks.live/definition/deep-out-of-the-money-options/", "headline": "Deep Out-of-the-Money Options", "description": "Low-cost derivative contracts used as insurance against extreme price movements due to their distance from market price. ⎊ Term", "datePublished": "2026-03-12T15:30:40+00:00", "dateModified": "2026-03-12T15:31:39+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/layered-collateralization-structures-for-options-trading-and-defi-automated-market-maker-liquidity.jpg", "width": 3850, "height": 2166, "caption": "A close-up view shows coiled lines of varying colors, including bright green, white, and blue, wound around a central structure. The prominent green line stands out against the darker blue background, which contains the lighter blue and white strands." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/off-chain-machine-learning/", "url": "https://term.greeks.live/term/off-chain-machine-learning/", "headline": "Off-Chain Machine Learning", "description": "Meaning ⎊ Off-Chain Machine Learning optimizes decentralized derivative markets by delegating complex computations to scalable layers while ensuring cryptographic trust. ⎊ Term", "datePublished": "2026-03-13T03:20:29+00:00", "dateModified": "2026-03-13T03:22:00+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/interoperable-asset-layering-in-decentralized-finance-protocol-architecture-and-structured-derivative-components.jpg", "width": 3850, "height": 2166, "caption": "A close-up view shows multiple strands of different colors, including bright blue, green, and off-white, twisting together in a layered, cylindrical pattern against a dark blue background. The smooth, rounded surfaces create a visually complex texture with soft reflections." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/penetration-testing-methodologies/", "url": "https://term.greeks.live/term/penetration-testing-methodologies/", "headline": "Penetration Testing Methodologies", "description": "Meaning ⎊ Penetration testing methodologies provide the essential mathematical and structural verification required to maintain solvency in decentralized derivatives. ⎊ Term", "datePublished": "2026-03-15T08:30:31+00:00", "dateModified": "2026-03-15T08:30:56+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/decentralized-finance-layer-two-scaling-solution-bridging-protocol-interoperability-architecture-for-automated-market-maker-collateralization.jpg", "width": 3850, "height": 2166, "caption": "A digital cutaway renders a futuristic mechanical connection point where an internal rod with glowing green and blue components interfaces with a dark outer housing. The detailed view highlights the complex internal structure and data flow, suggesting advanced technology or a secure system interface." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/machine-learning-finance/", "url": "https://term.greeks.live/term/machine-learning-finance/", "headline": "Machine Learning Finance", "description": "Meaning ⎊ Machine Learning Finance enables autonomous, adaptive risk management and optimized pricing within decentralized derivatives markets. ⎊ Term", "datePublished": "2026-03-15T10:26:24+00:00", "dateModified": "2026-03-23T21:26:25+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/decentralized-finance-composability-and-layer-2-scaling-solutions-representing-derivative-protocol-structures.jpg", "width": 3850, "height": 2166, "caption": "An abstract digital artwork showcases multiple curving bands of color layered upon each other, creating a dynamic, flowing composition against a dark blue background. The bands vary in color, including light blue, cream, light gray, and bright green, intertwined with dark blue forms." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/blockchain-network-security-methodologies/", "url": "https://term.greeks.live/term/blockchain-network-security-methodologies/", "headline": "Blockchain Network Security Methodologies", "description": "Meaning ⎊ Blockchain Network Security Methodologies provide the cryptographic and economic foundation necessary for trustless, irreversible financial settlement. ⎊ Term", "datePublished": "2026-03-16T23:28:59+00:00", "dateModified": "2026-03-16T23:30:07+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/optimized-algorithmic-execution-protocol-design-for-cross-chain-liquidity-aggregation-and-risk-mitigation.jpg", "width": 3850, "height": 2166, "caption": "A dark blue, streamlined object with a bright green band and a light blue flowing line rests on a complementary dark surface. The object's design represents a sophisticated financial engineering tool, specifically a proprietary quantitative strategy for derivative instruments." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/protocol-security-testing-methodologies/", "url": "https://term.greeks.live/term/protocol-security-testing-methodologies/", "headline": "Protocol Security Testing Methodologies", "description": "Meaning ⎊ Protocol security testing methodologies provide the essential frameworks to verify code integrity and economic resilience in decentralized finance. ⎊ Term", "datePublished": "2026-03-16T23:37:13+00:00", "dateModified": "2026-03-16T23:37:46+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/complex-crypto-derivatives-architecture-with-nested-smart-contracts-and-multi-layered-security-protocols.jpg", "width": 3850, "height": 2166, "caption": "An intricate geometric object floats against a dark background, showcasing multiple interlocking frames in deep blue, cream, and green. At the core of the structure, a luminous green circular element provides a focal point, emphasizing the complexity of the nested layers." } }, { "@type": "Article", "@id": "https://term.greeks.live/term/machine-learning-security/", "url": "https://term.greeks.live/term/machine-learning-security/", "headline": "Machine Learning Security", "description": "Meaning ⎊ Machine Learning Security protects decentralized financial protocols by ensuring the integrity of algorithmic inputs against adversarial manipulation. ⎊ Term", "datePublished": "2026-03-17T06:52:00+00:00", "dateModified": "2026-03-17T06:53:13+00:00", "author": { "@type": "Person", "name": "Greeks.live", "url": "https://term.greeks.live/author/greeks-live/" }, "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/green-underlying-asset-encapsulation-within-decentralized-structured-products-risk-mitigation-framework.jpg", "width": 3850, "height": 2166, "caption": "An abstract visual presents a vibrant green, bullet-shaped object recessed within a complex, layered housing made of dark blue and beige materials. The object's contours suggest a high-tech or futuristic design." } } ], "image": { "@type": "ImageObject", "url": "https://term.greeks.live/wp-content/uploads/2025/12/analyzing-interconnected-risk-dynamics-in-defi-structured-products-and-cross-collateralization-mechanisms.jpg" } } ``` --- **Original URL:** https://term.greeks.live/area/deep-learning-methodologies/resource/1/