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Deep Learning News
Medium
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How Generative AI is Changing Web Development
- Generative AI, a subset of AI, is revolutionizing web development by automating and enhancing various processes.
- AI-powered code generation tools assist developers in writing HTML, CSS, and JavaScript code, speeding up development and improving accuracy.
- Generative AI optimizes responsive design and website performance by automating layout adjustments and enhancing user experience.
- While AI offers valuable assistance, it is unlikely to replace human developers entirely, as creativity, problem-solving abilities, customization, and ethical considerations are crucial in web development.
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Medium
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323
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Unlocking Creative Magic: How LLM Prompts Revolutionize Writing & Beat Blocks
- LLM (Large Language Model) prompts can revolutionize creative writing by providing endless creative possibilities and breaking free from creative blocks.
- LLM prompts are a tech marvel and a canvas for writers to enhance creativity and find new inspiration.
- Fueled by AI innovations, LLM prompts act as a catalyst in creating unique and engaging stories.
- By using LLM prompts, writers can overcome the challenge of staring at a blank page and ignite their creativity.
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Medium
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422
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Ethical AI: Navigating the Risks and Rewards of Generative AI
- AI ethics is a hot topic, with generative AI at the forefront.
- As we embrace AI’s potential, we must address its ethical risks to ensure a responsible future.
- The article explores the balance between the immense potential and ethical challenges of AI.
- Generative AI, with its ability to create content, presents compelling possibilities as well as concerns related to copyright and misinformation.
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Medium
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179
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AI Meal Planning: Personalized Nutrition for Healthier, Smarter Eating
- AI meal planning offers personalized nutrition for healthier, smarter eating.
- AI can analyze dietary habits and health metrics to create tailored meal plans.
- It helps in making healthier choices without the hassle of meal planning.
- AI meal planning is like having a personal nutritionist at your fingertips.
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Medium
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359
Want to Make AI Work Better for You? Discover Assisted Programmatic Language (APL)!
- APL (Assisted Programmatic Language) is a technique that allows users to structure and improve their interactions with AI.
- With APL, users can define variables, create conditions, and use structured patterns to enhance AI responses.
- APL provides more control, precision, efficiency, and scalability in working with AI.
- By structuring prompts like a programmer, users can elicit smarter and more useful AI responses.
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Medium
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179
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Knowledge Graphs in AI 2025: Revolutionizing Transparency, Healthcare & Fraud Detection
- Knowledge graphs are reshaping AI by enhancing transparency, boosting performance, and enabling real-time decisions.
- They contribute to a new era of AI where data is not only collected but understood, and AI systems can explain their decisions.
- There are seven powerful types of knowledge graphs revolutionizing AI in 2025.
- Knowledge graphs have the potential to transform various industries including healthcare and fraud detection.
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Medium
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35
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How DLSS and FSR Are Changing the Future of Gaming Graphics
- DLSS (Deep Learning Super Sampling) by NVIDIA and FSR (FidelityFX Super Resolution) by AMD are reshaping the future of gaming graphics.
- DLSS uses deep learning algorithms to upscale images, improving clarity and sharpness without a significant performance hit.
- FSR is a universal technology that upscales images using spatial upscaling techniques, accessible to a wider range of hardware.
- Both DLSS and FSR balance performance and visual quality, providing significant improvements in frame rates and image quality.
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Medium
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48
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How Can Artificial Intelligence Detect Nudity on Human Body?
- Nudity Detection utilizes AI and computer vision to identify sensitive body parts in images or videos for content filtering purposes.
- It works by analyzing pixels, detecting human body shapes, and classifying skin exposure probabilities.
- Various AI methods are employed to detect nudity, such as using AI models like NudeNet.
- The process involves detector initialization, detection, analysis, visualization, determining image status, and saving results.
- Nudity Detection aids in filtering explicit content, especially on platforms with strict adult content regulations.
- It plays a crucial role in online privacy and security, particularly in content moderation and preventing the distribution of private images.
- Challenges include accuracy issues like false positives and ethical considerations around privacy rights infringement.
- Despite challenges, Nudity Detection is applied beyond social media, including e-commerce, cybersecurity, and parental control.
- Continuous AI advancements will enhance detection accuracy, reduce errors, and adapt to emerging internet content.
- Nudity Detection will evolve as a vital tool for ensuring digital security, privacy protection, and a safer online environment.
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Medium
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317
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32 is the Magic Number: Deep Learning’s Perfect Batch Size Revealed!
- While large batch sizes can improve computational parallelism, they may degrade model performance.
- Yann LeCun suggests that a batch size of 32 is optimal for model training and performance.
- In a recent study, researchers found that batch sizes between 2 and 32 outperform larger sizes in the thousands.
- Smaller batch sizes enable more frequent gradient updates, resulting in more stable and reliable training.
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Medium
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267
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Understanding the Differential Architecture search for Automating the design of Neural Networks.
- This article reviews four major gradient descent-based architecture search methods for discovering the best neural architecture for image classification: DARTS, PDARTS, Fair DARTS, and Att-DARTS.
- DARTS is a popular method that treats the architecture as a continuous variable and optimizes it using gradient-based methods. It uses a cell-based search space with operations such as convolutions, pooling, and identity.
- DARTS learns the cell architecture by searching for the optimal combination of operations. It uses eight cells, including normal and reduction cells, to design high-performance neural networks.
- The goal of DARTS is to learn the operation strength probabilities (α) and the optimal weights (ω) for constructing a cell and designing neural networks.
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Towards Data Science
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This Is How LLMs Break Down the Language
- LLMs, comprised of transformer neural networks and giant mathematical expressions, process input sequences through embedding layers converting tokens into numerical representations.
- During training, the neural network's billions of parameters are iteratively updated to align its predictions with patterns observed in the training set.
- The transformer architecture, introduced in 2017, serves as the foundation for LLMs and is specialized for sequence processing.
- Nano-GPT, with approximately 85,584 parameters, uses token sequences as inputs that undergo transformations to predict the next token in the sequence.
- Training a language model like ChatGPT involves stages like pretraining with a large dataset, such as FineWeb, to teach the model the flow of text.
- Tokenization, the process of converting raw text into symbols, is essential in LLMs and uses techniques like Byte-Pair Encoding to compress sequence length.
- Byte Pair Encoding involves identifying frequent symbol pairs to shorten sequences and expand the symbol set, with GPT-4 having a vocabulary size of around 100,000.
- Tools like Tiktokenizer allow for interactive exploration of tokenization models like GPT-4 base model, aiding in understanding how tokens correspond to text.
- State-of-the-art transformer-based LLMs rely on efficient tokenization strategies like Byte-Pair Encoding to process text inputs and enhance model performance.
- A well-designed tokenization approach is essential for improving the efficiency and overall performance of language models in processing and generating text.
- Understanding tokenization can provide insights into how LLMs interpret and generate text, contributing to advancements in model efficiency and effectiveness.
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Medium
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398
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Comprehensive Breakdown of Agentic AI vs Traditional AI: Future Trends, Ethics & Real-World Impact
- Agentic AI and Traditional AI are two giants in the field of artificial intelligence.
- Agentic AI is capable of autonomous decision-making and adapting to unpredictable situations.
- Traditional AI, on the other hand, follows set instructions and executes tasks flawlessly.
- Agentic AI represents a future trend in AI technology, while Traditional AI has a more limited role.
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Medium
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263
The Perceptron: A Teacher’s Surprising Connection to Deep Learning
- A teacher named Saad designs a simple scoring system for grading essays with the idea of a perceptron, a building block of deep learning.
- A perceptron is an artificial neuron that processes inputs, applies weights, and produces an output based on a threshold.
- The limitations of perceptrons are addressed with the use of activation functions, allowing for more human-like decisions, and bias, which adjusts the decision boundary.
- Perceptrons serve as the foundation for powerful neural networks in artificial intelligence applications like detecting spam emails, recognizing faces, and predicting diseases.
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Medium
3w
57
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AI the Guardian of Truth: Filtering Misinformation Without Censorship
- Artificial intelligence (AI) can be an effective and balanced solution for filtering misinformation without censorship.
- AI can act as a fact-checking assistant, providing users with context, sources, and alternative viewpoints to challenge misinformation.
- Intelligent filtering through AI ensures that misinformation is challenged with evidence, promoting truth without stifling free speech.
- By focusing on context, visibility adjustments, user choice, and transparency, AI can restore trust in online information while preserving open discourse.
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Medium
3w
106
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Comparing CNN and Prototypical Networks for Brain Tumor MRI Classification
- A comparative analysis was done between CNNs and prototypical networks to classify brain tumor MRI images.
- The CNN model was trained with only 5 images of the glioma class and performed well on classifying new images.
- The Prototypical Network, designed for few-shot learning, showed better generalization and improved accuracy in classifying different tumor types.
- Overall, the Prototypical Network was more effective in few-shot learning scenarios with limited data.
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