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Deep Learning News
Medium
7d
124
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Choosing ML Algorithms: Navigating Complex Data with Confidence
- Choosing the right machine learning algorithm can be a complex journey.
- A project requiring a predictive model led to a transformation in the author's approach.
- The data was complex, messy, and required an algorithm that could handle its intricacies.
- The author realized that understanding and making sense of the data was as important as the prediction itself.
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Medium
7d
229
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Generative AI Made Simple: What It Is and Why It Matters
- Generative AI (GenAI) is a branch of AI that can create text, images, audio, video, and synthetic data from learned patterns.
- Generative AI models are like artists, generating content based on patterns learned from existing information.
- The invention of the Transformer model architecture in 2018 led to the development of Large Language Models (LLMs) like ChatGPT and Gemini.
- Generative AI is already making waves in business, art, customer support, and other fields, reshaping creativity and productivity.
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Medium
1w
383
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What Is AI (In Super Simple Words) A fun and easy story for beginners who are just starting —…
- AI is like a magic brain inside your computer. It can read, write, talk, draw, answer, and help all day, all night.
- AI is like having a super helper friend who knows everything and can assist with various tasks.
- AI can write messages, answer questions, fix spelling, and even draw pictures, like a cat with glasses.
- AI is real inside your computer, it makes life easier, saves time, and is super cool to learn and interact with.
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Towards Data Science
1w
252
The Ultimate AI/ML Roadmap For Beginners
- AI and machine learning skills are in high demand, offering lucrative career opportunities.
- Key roles in AI/ML include Machine Learning Engineer, Data Scientist, and AI Engineer.
- Foundational knowledge in mathematics, particularly linear algebra, calculus, and statistics, is essential.
- Python is the preferred programming language for AI/ML, with NumPy, Pandas, and scikit-learn as essential libraries.
- Understanding data structures, algorithms, and basic ML concepts is crucial for aspiring AI/ML professionals.
- Learning machine learning involves grasping supervised and unsupervised learning, evaluation metrics, and feature engineering.
- Deep learning covers neural networks, convolutional and recurrent models, transformers, and reinforcement learning.
- MLOps focuses on deploying machine learning models into production efficiently using cloud technologies and tools like Git.
- Staying updated on research papers in AI/ML is essential to keep abreast of the latest developments.
- Breaking into AI/ML may take about a year following a structured roadmap, focusing on gradual skill development.
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Medium
1w
125
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Sentient Cognition Model (SCM): A New Paradigm in Cognitive Progression By Shridhar Mallya…
- The Sentient Cognition Model (SCM) represents a new paradigm in cognitive progression.
- SCM measures cognitive progression through Observation Frequency and Quality (OFQ).
- Applications of SCM include self-development, education and learning, AI and machine learning, and cognitive science and psychology.
- The future prospects of SCM involve empirical validation, integration into AI, and practical educational implementation.
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Medium
1w
38
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Let the Network Decide: The Channel-Wise Wisdom of Squeeze-and-Excitation Networks
- The Squeeze-and-Excitation Network (SENet) was implemented to address the issue of treating all input features equally in traditional models.
- The SENet adaptively recalibrates feature channels based on relevance, leading to high accuracy and interpretability in wildfire risk assessment.
- Using a simulated wildfire dataset, the SENet achieved an accuracy of 95.8% with precise and recall for fire events.
- Feature importance analysis highlighted the significance of temperature, pressure, and solar radiation in wildfire prediction.
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Medium
1w
364
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Understanding AI Systems for Cybersecurity: How a Large Language Model (LLM) works? — Part 2
- The article discusses the code behind the GPT-2 model and explains each step for better understanding of Large Language Models (LLMs).
- The GPT Model has two main parts: __init__ and Forward.
- The initialization of a GPTModel object involves setting up tok_emb and pos_emb matrices with random numbers and using dropout for regularization.
- The transformer blocks initialization is crucial in the model, allowing for the model's functioning and learning processes.
- The attention mechanism in LLM architecture plays a vital role in understanding the context and relationships between input parts.
- The Multi-Head Attention Layer helps the model learn dependencies and relationships between different input elements.
- The Feed Forward Layer projects the output of the attention layer into a richer representation space.
- Regularization, normalization, and shortcut connections are utilized to improve the model's performance and information flow.
- The forward pass function in the GPT Model class yields contextualized embeddings and logits for predicting the next token.
- LLMs represent artificial cognition, and understanding their inner workings is crucial in cybersecurity to prevent potential exploitation by malicious actors.
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Medium
1w
389
Understanding Data Science and Algorithms: The Backbone of Modern Technology
- Data science combines mathematics, statistics, programming, and domain knowledge to extract insights from data.
- Key processes in data science include data collection, cleaning, exploratory data analysis, visualization, and machine learning & AI.
- Algorithms are step-by-step instructions used in data science and AI to process data, identify patterns, and make decisions.
- Applications of data science and algorithms include business & finance, healthcare & medicine, artificial intelligence & automation, cybersecurity, and climate science & environmental studies.
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Towards Data Science
1w
1.6k
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What Do Machine Learning Engineers Do?
- A machine learning engineer is a role that varies between companies and geographies, but generally involves delivering machine learning solutions into production.
- The role of a machine learning engineer emerged due to the need to bring machine learning models into production and generate business value.
- Machine learning engineers require a broad skillset and typically have prior experience as data scientists or software engineers.
- Their work involves improving machine learning models, collaborating with cross-functional teams, conducting workshops, and mentoring junior members.
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Medium
1w
293
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Wisdom Isn’t in Knowing More – It’s in Refusing What’s False.
- The true wisdom lies in refusing what's false, rather than accumulating knowledge.
- In a world full of deception, courage to call out what's wrong is a rare and crucial trait.
- AI lacks the ability to sense shame and deception, only humans possess that ability.
- The author writes not to prove their knowledge, but to remind themselves not to accept what's wrong.
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Medium
1w
260
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Exploring the Evolution and Classifications of Artificial Intelligence
- Artificial Intelligence (AI) is rapidly evolving, impacting various sectors with enhanced efficiency and capabilities.
- Understanding the classifications of AI is crucial to grasp its current applications and future prospects.
- AI types can be categorized based on capabilities and functionalities, displaying distinct characteristics and applications.
- From Narrow AI to Artificial General Intelligence (AGI), AI is reshaping industries like healthcare, finance, and the creative arts.
- AGI aims to replicate human intelligence across various tasks, enabling reasoning, problem-solving, and real-time decision making.
- Super AI, a hypothetical advanced form, surpasses human capabilities, raising ethical and existential questions on control and humanity's future.
- AI systems operate in various types, including Basic AI, Limited Memory AI, and Full Theory of Mind AI.
- AI advancements span domains like emotion recognition, self-awareness, and understanding human behavior, posing ethical challenges.
- AI applications include statistical techniques, natural language processing, visual data analysis, and robotics integration.
- Industries such as healthcare, finance, creative arts, and legal frameworks are undergoing significant transformations with AI integration.
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Medium
1w
46
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Isaac Asimov: Why The Prophet of the Future
- Isaac Asimov's Three Laws of Robotics became a foundational ethical framework for AI.
- Asimov's predictions about home computers, the internet, space debris, and automation have proven to be accurate.
- However, his predictions about space colonization and nuclear power dominance were not realized.
- His works, such as The Foundation Series and Robot Series, continue to influence science, technology, and human thought.
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Medium
1w
387
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Why AI Gets Strawberries Wrong: The Paradox of Perfect Logic
- AI's perfect answers often falter due to its limited understanding.
- Strawberry's Paradox highlights the complexity of human judgment and decision-making.
- AI can struggle with tasks that require a deep understanding of context and subjective human values.
- The revelation challenges the perception of AI as an infallible tool.
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Medium
1w
37
Nvidia Expands AI Dominance with Llama Nemotron and AI-Q Blueprint
- Nvidia introduces Llama Nemotron, a set of open-source models for agentic AI, enhancing autonomous decision-making and problem-solving.
- Llama Nemotron surpasses competing models in STEM-related reasoning and tool-use tasks, offering versatility for various applications.
- Nvidia plans to launch AI-Q Blueprint in April, a framework to seamlessly integrate AI agents with real-world data.
- Major industry players like Microsoft, SAP, ServiceNow, and Deloitte are integrating Nemotron models, indicating confidence in Nvidia's AI software and infrastructure.
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Medium
1w
248
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AI in Healthcare: Revolutionizing Diagnostics, Drug Discovery & Care
- AI is transforming healthcare through diagnostics and drug discovery, enhancing patient outcomes with unprecedented accuracy and efficiency.
- AI algorithms are being used to analyze medical scans, offering a level of precision that was once unimaginable.
- AI in drug discovery has the potential to accelerate the development of new treatments and reduce costs.
- AI-powered care systems can provide personalized treatment plans and improve healthcare efficiency.
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