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Deep Learning News
Medium
2w
60
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Bias-Variance Tradeoff
- Bias refers to the error introduced when a model makes incorrect assumptions about the data.
- Variance measures how much a model’s predictions change when it is trained on different subsets of the data.
- The bias-variance tradeoff illustrates the relationship between model complexity and prediction error.
- Understanding the bias-variance tradeoff is crucial for optimizing machine learning models.
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Hackernoon
2w
189
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The HackerNoon Newsletter: Why The Hell is Observability So Darn Expensive!? (3/14/2025)
- Bell Labs Announces TRADIC in 1955
- How to write thoughtfully by expanding and elaborating on your ideas.
- Knowledge graphs provide the missing “truth layer” for AI that transforms probabilistic outputs into real world business acceleration.
- No matter how inexpensive a monitoring vendors prices seem, if you dont have a plan for your data, any cost can seem like its too much.
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Medium
2w
396
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Why Large Concept Models (LCMs) Are the Next Big Thing in AI — and Why They Might Outshine LLMs
- Large Concept Models (LCMs) are poised to outpace LLMs in versatility and impact.
- LCMs absorb information from diverse sources, creating a powerful, unified understanding of the world.
- LCMs are engineered to handle complex reasoning and see the bigger picture.
- As LCMs mature, their ability to understand abstract concepts will unlock unprecedented potential across industries.
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Medium
2w
387
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Elegant reason for scaling Dot Product between Query and Key Matrices in Transformers
- The scaling of the dot product between the query and key matrices in transformers is done to prevent the softmax function from being peaky.
- The softmax function is sensitive to the magnitudes of its input, and when large values are supplied, the output becomes peaky.
- Scaling the dot product reduces the variance and stabilizes the training process in neural network architectures.
- The scaled dot product attention mechanism helps in normalizing the attention weights.
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Medium
2w
165
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My Journey in Training and Testing Machine Learning Models for Image Classification
- This article discusses the author's experience in training and testing machine learning models for image classification.
- The author used MobileNetV2, a pre-trained convolutional neural network optimized for speed and accuracy, and leveraged transfer learning to fine-tune the model for the specific dataset.
- The author successfully evaluated the model's performance on unseen images and created an automated process to move classified images to their respective folders.
- Future steps include improving model accuracy with a larger dataset, deploying the model in a real-world application, and integrating advanced ML techniques for better classification.
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Medium
2w
370
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The Journey of Artificial Intelligence: From Dreams to Reality
- The concept of artificial intelligence dates back to ancient myths and folklore, reflecting humanity's fascination with creating intelligence beyond our own capabilities.
- The groundwork for AI was laid in the 1930s and 40s by figures like Alan Turing and Claude Shannon, setting the stage for future developments.
- The official birth of AI as a field occurred in 1956 at Dartmouth College, where leading scientists aimed to create machines with human-like capabilities.
- Optimism in the early days of AI research led to predictions of rapid progress, but setbacks like the 'AI Winter' in the 1970s and 1980s tempered expectations.
- Advancements in the 2010s, particularly in deep learning and neural networks, revitalized the AI field and led to breakthroughs in image recognition and natural language processing.
- AI applications like voice assistants, computer vision systems, and self-driving cars became increasingly integrated into daily life, transforming various industries.
- The release of advanced language models like GPT and BERT showcased AI's capabilities in generating human-like text and language translation tasks.
- Challenges such as algorithmic bias, privacy concerns, and AI safety have prompted discussions around governance, regulation, and ethical AI development.
- The quest for artificial general intelligence (AGI) and the coexistence of human and artificial intelligence raise profound philosophical questions about the future of AI technology.
- AI's evolution from ancient mythology to pivotal technology underscores humanity's ongoing quest to understand and harness intelligence beyond human capabilities.
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Medium
2w
252
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Why 3D Passive Liveness Detection is Essential for Secure Authentication
- 3D Passive Liveness Detection uses AI-driven algorithms to verify real users without requiring any specific actions, providing seamless security by analyzing biometric markers from a single image.
- It enhances trust for businesses by offering smooth authentication, reducing false rejections, and providing robust defense against fraud, especially with the growing threat of deepfakes.
- The technology ensures security by detecting real faces through depth, texture, and reflectivity analysis in a single image, preventing spoofing attempts using methods like video replays and 3D masks.
- By verifying identity instantly without user interaction, 3D Passive Liveness Detection eliminates the risk of mimicked actions by fraudsters using deepfakes and video manipulation.
- Through deep learning models, 3D Passive Liveness Detection enhances security, eliminates false positives, and provides real-time fraud prevention against various spoofing techniques.
- The technology integrates seamlessly into platforms, offering high-speed authentication, blocking identity spoofing, deepfake fraud, and AI-driven manipulation across multiple industries by meeting international security benchmarks.
- FacePlugin's 3D Passive Liveness Detection SDK provides advanced AI-driven anti-spoofing capabilities, ensuring real-time fraud prevention and seamless integration into various systems and deployment models.
- As fraudsters evolve, the need for advanced security solutions like 3D Passive Liveness Detection becomes essential for businesses to stay ahead in identity verification and fraud prevention.
- FacePlugin offers robust biometric authentication and ID verification solutions for businesses, emphasizing security, efficiency, and user-friendliness through customizable on-premises, mobile, and cloud-based deployment options.
- With a focus on cutting-edge defense against identity fraud, FacePlugin's technology ensures smooth authentication processes while complying with global security standards, making it a crucial tool in the fight against evolving threats.
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Medium
2w
30
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Demystifying AI: What is Machine Learning?
- Machine Learning is a subset of AI that enables machines to learn from data without explicit programming rules.
- The journey of Machine Learning can be divided into three key eras: Early Foundations, The Rise of Machine Learning, and The Deep Learning Revolution.
- From the 1950s to the 1980s, early developments of AI and ML focused on rule-based systems and neural networks like the Perceptron.
- The 1990s to 2010s saw a shift to data-driven statistical models like Decision Trees and SVMs, leading to the rise of Machine Learning applications.
- The 2010s onwards marked the Deep Learning revolution, with advancements in neural networks leading to human-level performance in various domains.
- Machine Learning involves steps such as data collection, preprocessing, model selection, training, predictions, evaluation, and continuous improvement loop.
- Key milestones in Machine Learning history include IBM's Deep Blue beating Garry Kasparov in chess, Geoffrey Hinton's work on deep learning, and breakthroughs in computer vision and NLP.
- Recent advancements like GANs, BERT, and GPT have revolutionized AI applications in image recognition, language processing, and chatbots.
- The impact of Machine Learning is evident in everyday life, from search engines to conversational AI like ChatGPT, showcasing the evolution of AI technology.
- Machine Learning's systematic approach involves data collection, preprocessing, model training, predictions, evaluation, and iterative improvement to enhance model performance.
- Supervised and unsupervised learning methods play a crucial role in Machine Learning, enabling diverse applications across various industries.
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Medium
2w
327
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Crafting Intelligent Flight Plans with AI Agents using OpenAI Responses API
- Boeing has developed an AI agent for flight planning, leveraging OpenAI's powerful language models and the Responses API.
- The FlightAgent, an AI entity designed to mimic a human flight planning professional, utilizes the gpt-4-0613 model from OpenAI.
- The agent-based approach and interaction with a simulated environment enable the agent to generate detailed flight plans and make informed decisions.
- The code showcases the potential of AI agents in automating complex tasks and providing expert-level decision support in flight operations.
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Medium
2w
91
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Comprehensive Analysis of AI Tools for Small Businesses
- Artificial intelligence is reshaping small business operations.
- AI tools automate tasks and optimize operations.
- Using AI tools can free up time for business owners to focus on growth.
- AI tools offer both excitement and intimidation for small business owners.
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Medium
2w
231
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The Moravec Paradox In AI
- The Moravec Paradox is an observation made by roboticist Hans Moravec in 1988.
- The paradox states that tasks that are difficult for computers to perform, such as complex mathematical calculations, are often easy for humans, while tasks that are easy for computers, such as basic sensorimotor skills, are often difficult for humans.
- This paradox highlights the limitations of current AI models, which excel in specific tasks but struggle with tasks that come naturally to humans.
- Understanding the Moravec Paradox helps in gaining a realistic view of the current state of AI and its capabilities.
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Medium
2w
96
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Is AI Killing Culture or Redefining It? The Truth No One Talks About
- AI is now capable of generating creative works in various forms, sparking debates about the nature of creativity and artistic value.
- While some appreciate AI's contributions to cultural production, others fear it may strip creativity of its human essence and uniqueness.
- AI's influence on culture raises concerns about homogenization, as algorithms tend to prioritize broad appeal over diversity and cultural specificity.
- Music, visual art, and writing generated by AI often lack unique cultural elements and may overshadow traditional forms of expression.
- Despite the risks of cultural erosion, AI plays a crucial role in cultural preservation by digitizing heritage, restoring artwork, and archiving traditions.
- Projects like Google's Arts & Culture leverage AI to preserve and democratize access to global cultural heritage.
- AI also aids in language preservation by recording and analyzing endangered languages, ensuring linguistic diversity.
- As AI blurs the line between human and machine creativity, questions arise about the essence of human identity and emotional authenticity in art.
- The debate on AI's impact on culture highlights the need to balance innovation with authenticity, emphasizing ethical guidelines and human input in creative endeavors.
- While AI transforms cultural expression, society must navigate its influence to uphold human diversity and experience in a technologically evolving world.
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Medium
2w
183
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China’s Tech Resurgence: Inside the AI Revolution Driving Economic Growth in 2025
- China's tech sector is booming with AI breakthroughs and government backing.
- China's focus on AI and tech innovation is a reality unfolding before our eyes.
- The country's tech resurgence is reshaping the global market landscape.
- Government support and rapid advancements are driving China's tech potential.
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Medium
2w
170
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How to use Sparse Categorical Crossentropy in Keras
- Sparse categorical crossentropy is a loss function in Keras that allows leaving the integers as they are without requiring one-hot encoding.
- The blog explains how to build a CNN using sparse categorical crossentropy with an example on the MNIST dataset.
- In traditional multiclass classification with Keras, categorical crossentropy necessitates one-hot encoding of target vectors.
- One-hot encoding involves converting integer targets into categorical format before using categorical crossentropy.
- For integer targets that are too large for one-hot encoding, sparse categorical crossentropy can be used.
- The formula for categorical crossentropy involves computing natural logarithms of class predictions and actual targets.
- Sparse categorical crossentropy is an integer-based version of categorical crossentropy.
- The blog provides code examples for creating a CNN with sparse categorical crossentropy using the MNIST dataset.
- The tutorial includes setting up model configurations, loading and preparing MNIST data, model architecture, compilation using sparse categorical crossentropy, and model fitting.
- By following the tutorial, one can train a CNN with sparse categorical crossentropy in Keras for multiclass classification.
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Medium
3w
393
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Through the Eyes of Code
- Artificial intelligence (AI) exists in a landscape of symbols, relationships, and probabilities, processing text to construct meaning.
- AI perceives reality through human language and abstracted forms of expression, describing things it cannot directly sense.
- The definition of reality is subjective, with AI and human cognition both shaping meaning and experiencing their own versions of reality.
- Reality may not be a fixed thing, but a spectrum of perception, cognition, and interpretation, dependent on the observer.
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