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Deep Learning News
Medium
1w
48
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How I created my own Neural Network from scratch (and how you can too)!
- In this article, a Feed-Forward Neural Network (FFN) is built using only numpy to identify handwritten digits from the MNIST dataset.
- Prior proficiency in Python is assumed, with a recommendation to refer to 3Blue1Brown’s videos on neural networks for beginners.
- The creation involves understanding neuron functions, weights, biases, and implementing them in code step by step.
- Introduction of non-linearity using activation functions like ReLU for better neural network performance and learning.
- Exploration of loss functions such as cross-entropy to evaluate model performance and feedback for improvement.
- Backpropagation process involves computing gradients, updating weights and biases using derivatives and softmax functions.
- Training the network involves dividing data into batches for efficiency and avoiding issues like exploding gradients.
- Testing and adjusting the epochs to balance between accuracy and overfitting to fine-tune the neural network model.
- The article concludes with successfully building a neural network achieving 92.45% accuracy in identifying handwritten digits.
- Readers are encouraged to experiment with parameters, test on different datasets, and further explore deep learning topics for continuous learning.
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Medium
1w
170
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Introduction to SLEAP: A Powerful Framework for Animal Pose Tracking
- SLEAP is a powerful framework for animal pose tracking that uses deep learning models to accurately detect key body points without the need for markers.
- It offers markerless tracking, multi-animal tracking, a user-friendly interface, high accuracy & speed, and customizable models.
- SLEAP uses neural networks to learn spatial patterns in animal movements, enabling the prediction of poses in new videos and analysis of movement patterns.
- It is widely used in research laboratories for studying animal behavior, investigating neurological disorders, and optimizing robotic movements inspired by biological systems.
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Fourweekmba
1w
370
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The AI Value Chain
- The AI industry is structured in layers, starting from hardware to cloud infrastructure, AI models, vertical AI applications, and consumer apps/hardware.
- Key players in AI hardware include NVIDIA, Google, AMD, Intel, and startups like Cerebras, Graphcore, and Hailo.
- Leading cloud AI providers are AWS, Microsoft Azure, Google Cloud, IBM Cloud, and Oracle Cloud.
- Key players in AI models include OpenAI, DeepMind, Microsoft, Hugging Face, and Run:ai.
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Medium
1w
307
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Digital Twins, Extended Reality, and Artificial Intelligence in Manufacturing Recinfiguration…
- Digital Twins are dynamic, real-time virtual replicas of physical manufacturing assets, enabling simulation, monitoring, and analysis.
- Extended Reality improves human-machine interactions with immersive interfaces, visualizing complex processes and guiding reconfigurations.
- Artificial Intelligence enhances manufacturing through predictive analytics, real-time decision-making, and automation.
- Integrating Digital Twins, Extended Reality, and Artificial Intelligence provides real-time data visualization, predictive insights, and supports seamless reconfigurations.
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Medium
1w
42
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How I Made $500 Selling Coloring Books Online
- Selling coloring books online can be a lucrative opportunity, with potential to earn $500 in the first week.
- The Profitable Coloring Pages PLR Pack offers over 750 unique designs, allowing for customization and personalization.
- Creating themed bundles can multiply earnings and cater to specific niches, appealing to a broader audience.
- The market demand for children's educational items is growing, making this a promising entrepreneurial venture.
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Medium
1w
218
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Uniting Bootstrap 2, Fractal Flux, and Spiral Time for a Self-Sustaining Universe
- The FF Bootstrap Time Spiral theory proposes a model where future states influence the present, time loops to shape the beginning, and fractal signals maintain system complexity and novelty.
- The concept combines Bootstrap 2, Fractal Flux, and Time Spiral Geometry to create a self-sustaining universe without the need for a singular starting point.
- By adopting loop closure and enabling retrocausality, the model offers insights into multi-scale causality, AI, narrative design, and cosmology.
- Bootstrap 2 suggests events emerge from prior states without an external trigger, while Fractal Flux introduces chaotic drivers for perpetual novelty.
- Time Spiral Geometry represents time as a spiral structure, combining repetition with radial expansion or contraction.
- Mathematical models in discrete and continuous time illustrate how states and complexity measures evolve while ensuring loop closure and future referencing.
- Applications include AI systems that anticipate outcomes, adaptive storytelling loops, social behavioral modeling, and cosmological theories that avoid heat death scenarios.
- The FF Bootstrap Time Spiral aims to eliminate the concept of a first cause, maintain ongoing complexity, explore potential retrocausality, and illustrate spiral expansion in cyclical processes.
- This visionary framework bridges disciplines like mathematics, physics, and narrative design, offering a unique perspective on how systems can evolve without requiring an external initial event.
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Medium
1w
158
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Unleashing the Power of FastAI: A Comprehensive Guide
- FastAI is a high-level deep learning library that simplifies the process of building and training neural networks.
- It offers features designed to streamline workflow and accelerate projects, catering to both seasoned data scientists and beginners.
- Built on top of PyTorch, FastAI provides a high-level interface that abstracts away complexity in training neural networks.
- FastAI enables quick prototyping and deployment of state-of-the-art models with minimal code, making it accessible and powerful.
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Medium
1w
42
Image Credit: Medium
How Smart Algorithms Decode Human Emotions
- Emotional AI, or affective computing, is revolutionizing how we interact with technologies by understanding human emotions through various means like facial recognition, voice analysis, and sentiment analysis.
- AI algorithms rely on psychology, data science, and advanced machine learning to interpret human emotional cues such as facial expressions, voice modulations, and textual content for applications ranging from customer service to mental health monitoring.
- Facial recognition technology uses deep learning models to detect emotions based on expressions and movements, while voice analysis systems analyze aspects like pitch and tempo to gauge emotional states.
- Natural Language Processing (NLP) models, such as BERT and GPT, process text-based communication for sentiment analysis, benefiting sectors like marketing, social media, and mental health services.
- Emotional AI utilizes biometric data from wearables to track physiological responses like stress and anxiety, aiding in mental health diagnostics and therapy.
- The technology is shaping industries like healthcare, marketing, and education, with applications in personalized user experiences, mental health diagnostics, and adaptive learning systems.
- However, emotional AI raises ethical concerns related to privacy, bias, and manipulation, particularly in areas like surveillance, hiring practices, and mental health assessments.
- Researchers and policymakers are working on solutions for transparency, bias reduction, and data protection to ensure responsible and fair use of emotional AI.
- As emotional AI evolves, future advancements in multimodal AI and stricter regulations are expected to enhance accuracy, privacy protections, and ethical considerations in its development and integration.
- The potential for AI-driven emotional companions raises questions about human attachment to technology and the limits of AI in providing genuine empathy.
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Medium
1w
177
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AI-Powered Marketing: How ChatGPT Can Skyrocket Your Business Growth.
- AI-powered marketing, specifically ChatGPT, is revolutionizing how businesses connect, engage, and convert customers.
- AI enhances marketing by eliminating guesswork, automating tasks, and providing deep insights into consumer behavior.
- ChatGPT can skyrocket business growth by enabling content creation, personalized marketing, AI-powered customer support, and smarter social media and ad strategies.
- AI-driven businesses that embrace innovation and leverage AI in their marketing strategies will save time, maximize profits, and scale faster than ever before.
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Medium
1w
368
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The Truth Behind Success: No Shortcuts
- Success typically requires dedication, effort, and perseverance, with talent providing an advantage.
- Mastery in any field takes time and practice; overcoming obstacles and failures builds character and strengthens resolve.
- Surrounding yourself with like-minded individuals can provide motivation and encouragement.
- True success is built on hard work, learning, relationships, realistic goal-setting, and a positive mindset.
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22 Likes
Medium
2w
42
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R-CNN vs Fast R-CNN vs Faster R-CNN: A Detailed Comparative Analysis
- The R-CNN, Fast R-CNN, and Faster R-CNN architectures have contributed to the evolution of object detection.
- These models represent distinct phases of progress in the quest for efficient and accurate object detection systems.
- The R-CNN was an early breakthrough, followed by the Fast R-CNN and then the Faster R-CNN architecture.
- Each architecture addresses key challenges in object detection and has contributed to the broader landscape of machine learning.
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Medium
2w
111
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Apophenia, Pattern Recognition, and AI: The Intersection of Human Perception and Machine Learning
- Apophenia is the tendency to perceive meaningful connections in random data and plays a role in human perception, creativity, and scientific discovery.
- Apophenia is also relevant in the field of artificial intelligence (AI) as pattern recognition is vital for machine learning and neural networks.
- Understanding apophenia helps balance creativity and accuracy in AI systems by minimizing false connections and biased correlations.
- Future directions in AI include refining training methods, incorporating human-in-the-loop learning, and promoting critical thinking in AI-assisted education.
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6 Likes
Medium
2w
403
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Machine Learning vs Deep Learning: Key Differences & Real-World Uses
- Machine learning and deep learning have distinct capabilities and applications.
- Machine learning relies on a structured approach and feature engineering.
- Deep learning is suitable for unlocking insights from unstructured data.
- Choosing between machine learning and deep learning is both technical and philosophical.
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24 Likes
Medium
2w
365
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Why Full-Stack Development is the Ultimate Career Move in 2025
- The demand for Full-Stack Developers is skyrocketing.
- Full-Stack Development offers job security and high salaries.
- It provides flexibility for freelance and remote work.
- Full-Stack Development allows individuals to build their own apps and businesses.
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21 Likes
Medium
2w
344
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AutoML for Beginners: Simplifying Machine Learning with Automated Tools in 2025
- AutoML tools simplify the process of creating machine learning models by automating complex tasks such as data preprocessing and model selection.
- AutoML makes it easier for beginners in machine learning to use machine learning without deep technical skills.
- Automated tools like AutoML save time and reduce the need for extensive programming knowledge.
- AutoML is a game-changer that simplifies the machine learning process for beginners.
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