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Deep Learning News
Medium
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Theory to practice: a daily challenge in bridging the gap
- Bridging the gap between theory and practice is a daily challenge in teaching math.
- Students often understand the theory behind math concepts but struggle to apply them in practical situations.
- Teachers must help students concretize abstract ideas and demonstrate the practicality of math in daily life.
- Teachers need to be flexible, adaptable, and constantly learn from student responses to improve their teaching methods.
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Medium
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Self-Driving Cars: The AI Brains Behind the Wheel
- Autonomous vehicles rely on various sensors including cameras, radars, lidars, ultrasonic sensors, and IMUs to detect and monitor traffic flow, estimate distances and observe their environment.
- Artificial intelligence (AI) systems are the key to self-driving cars performing four core functions including perception, prediction, planning, and control.
- High-definition maps are used to determine accurate locations and plan routes for autonomous vehicles safely.
- Vehicle-to-everything (V2X) communication, which includes V2V, V2I and V2P communications, is crucial for enabling advanced driving automation and smart mobility.
- Safety and public acceptance are the top concern while cost is another major challenge to autonomous vehicle technology implementation.
- As autonomous vehicle companies and policymakers overcome these challenges, a safe, affordable, and trusted autonomous vehicle ecosystem will emerge, ushering in a new era of intelligent transportation.
- Self-driving technology is progressing fast. However, there are still challenges around standardization, security, and deployment at scale.
- AI can handle the complexity of driving without input from humans.
- The rapid pace of AI advancement means autonomous cars will soon hit the streets, but it may take a generational shift in mindset before fully autonomous cars become mainstream.
- Autonomous vehicles' sensors and AI must work together with V2X communication and detailed maps for precise and safe autonomous driving.
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Nvidia
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Needle-Moving AI Research Trains Surgical Robots in Simulation
- A collaboration between NVIDIA and academic researchers has developed ORBIT-Surgical, a simulation framework to train surgical robots.
- The framework supports maneuvers inspired by laparoscopic procedures and reduces surgeons' cognitive load.
- Using NVIDIA Isaac Sim and reinforcement learning algorithms, the researchers trained a digital twin in simulation to transfer skills to a physical robot.
- ORBIT-Surgical introduces benchmark tasks for surgical training and enables high-fidelity synthetic data generation for training AI models.
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Medium
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10 Game-Changing Habits to Solve of Your 90% Problems
- Reflect on three things you're grateful for each day. It shifts focus from problems to blessings.
- Truly listen to others without interrupting. It fosters better understanding and stronger relationships.
- Identify the most important tasks each day and tackle them first. It prevents feeling overwhelmed and increases productivity.
- Learn to say no when necessary. It protects your time and energy for things that truly matter.
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Medium
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A Beginners Guide to PyTorch
- PyTorch is an open-source library for building neural networks.
- PyTorch is more Python-friendly and intuitive compared to TensorFlow.
- Basic operations such as tensor initialization, indexing, and arithmetic are explained.
- A basic PyTorch neural network is built, and training using DataLoader, loss function, and optimizer is covered.
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Medium
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Supercharge Your Learning: Easy Tricks to Stay Motivated and Learn Better.
- Turn Learning into a Game: Break your tasks into small challenges and reward yourself when you complete them.
- Picture Your Goals: Imagine yourself reaching your goals to stay motivated.
- Take Things One Step at a Time: Break big tasks into smaller, manageable pieces.
- Find Friends to Study With: Work together with classmates to stay motivated.
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Medium
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Exploring Deep Learning: Utilizing Convolutional Neural Networks (CNN) for Household Item…
- In this article, the author explores how Convolutional Neural Networks (CNNs) can be used to bring artificial intelligence into the realm of household item recognition.
- CNNs have revolutionized the way we approach tasks such as image recognition and classification.
- CNNs can autonomously classify various household items depicted in images, paving the way for a multitude of applications in image processing.
- The article explains the CNN architecture which is divided into 2 parts: Feature Extraction Layer and Fully-Connected Layer (MLP).
- The Feature Extraction Layer consist of Convolutional Layer and Pooling Layer. Convolutional Layer comprises multiple learnable filters or kernels, which are small-sized matrices.
- The Fully-Connected Layer consists of neurons arranged in a fully-connected manner and learns the relationships between these features and the corresponding class labels.
- The article also explains the data preparation process, data augmentation, and model training.
- Regularization and data augmentation are two effective techniques for combating overfitting in CNN models.
- By incorporating regularization and data augmentation techniques, CNN models can learn meaningful and generalizable features from the training data.
- By using CNN models to classify products from unseen images, the model can learn and make better predictions.
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Medium
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Music Recommender System Using Python
- This project focuses on building a music recommender system using Python.
- The project utilizes the spotify_millsongdata dataset from Kaggle, consisting of 1 million song records.
- The data is preprocessed by removing unnecessary characters, converting text to lowercase, and using the PorterStemmer algorithm for word normalization.
- The TF-IDF approach is employed to convert the text into numerical vectors and calculate cosine similarity between songs.
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Medium
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FIRST PRINCIPLES
- The purpose of the creation of the universe.
- How questions actually function.
- The definition of consciousness and materialism.
- Exploring the relationship between spirituality and materialistic lifestyle.
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Medium
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Moore-Penrose Inverse and it’s connection to Double Descent phenomenon and Implicit Regularization
- The double descent curve exhibits a second descent in generalization error when the model complexity surpasses a certain threshold which occurs at d = n called the interpolation threshold.
- The Moore-Penrose inverse is a generalization of the matrix inverse that extends its applicability to rectangular and singular matrices.
- The Moore-Penrose Pseudoinverse provides a way to handle cases where matrix inversion is not defined for matrices that are not square.
- Implicit regularization refers to the inherent bias of certain learning algorithms towards solutions with desirable properties, such as smoothness or simplicity, without explicitly imposing regularization penalties.
- The minimum norm solution obtained in the overparameterized regime can be seen as a form of implicit regularization.
- In the overparameterized regime, the pseudoinverse solution has the minimal Euclidean norm among all possible solutions, exhibiting an implicit regularization property.
- Moore-Penrose inverse sheds light on the surprising generalization performance of overparameterized models.
- Understanding the mechanisms behind implicit regularization can guide the design of novel learning algorithms that can effectively balance the expressiveness of over-parameterized models with the desire for generalization.
- The implicit bias of the unregularized minimum norm estimator precisely corresponds to solving a ridge-regularized least squares problem on the population distribution.
- Exploring the over-parameterized regime and leveraging the implicit regularization properties of learning algorithms may lead to improved performance and simplify the model development process.
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Medium
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LLMs don’t even do ‘approximate retrieval’ — embarrassingly, they try to recall some ‘similars’
- Counterfactual task is an ideal task to test LLMs by exposing them to data that is unlikely to have been seen during training.
- Disrupting the training 'template' of LLMs results in a drop in performance to near-random choice.
- Experiments conducted on GPT 4o reveal that LLMs are essentially massive fuzzy hashtables and do not understand language or reasoning.
- A purely behavioristic, associative, statistical paradigm cannot explain cognition, language, reasoning, understanding, and the mind.
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Medium
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DL RSS Based Fingerprint
- RSS-Based fingerprinting is a low-cost, practical and efficient way of positioning. It uses signal strength measurements from smartphones: Offline phase comprises the building of RSS fingerprinting database while the Online phase consists of comparing real-time signal measurements with the entire fingerprint database for location estimation.
- Traditional RSS-based fingerprinting may face limitations in accuracy and scalability, particularly in large-scale outdoor environments.
- The integration of deep learning with RSS-based fingerprinting helps in improving its accuracy and scalability, offering precise, reliable outdoor positioning solutions for location-based services and applications.
- Outdoor positioning can be based on various positioning techniques: proximity sensing, lateration, angulation, dead reckoning, fingerprinting and Global Navigation Satellite Systems (GNSSs).
- RSSI, RSRP, and RSRQ are considered effective parameters for RSS-Based Fingerprinting algorithms.
- Deep learning helps to convert signal data into images and extract signal features to solve issues with existing matching algorithms. It involves converting the location problem into a classification problem.
- The proposed Deep Learning-based outdoor positioning system divided an area into multiple grids and used the United Direct Fingerprint.
- The system comprises four parts: LTE signal collection, LTE data pre-processing, training phase, and matching phase.
- The system uses Resnet model and a Feed-Forward Neural Network (FFNN). The outputs of the FFNN represent the probabilities of all grid-based positions.
- The integration of Deep Learning into RSS-Based Fingerprinting, offer promising practices for improved performance and utility in outdoor location-based services.
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Medium
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337
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"The Brain Anticipates the Future: A Hypothesis"
- The author proposes a hypothesis regarding the brain's ability to anticipate future actions.
- The subconscious mind plays a role in influencing our decisions and actions.
- The author claims the theory is original and seeks feedback and support.
- The hypothesis suggests that our minds can change decisions based on current feelings and task difficulty.
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Medium
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OSU teacher assists Companions for People Detainee With hounding Preparing Project
- The Oklahoma State University (OSU) is running a program that provides training and education for detainees in hounding.
- The program includes housing up to 20 canines in a dedicated facility, where prisoners learn about canine training and are given educational resources.
- During the 11-week program, prisoners learn patience, provide consistent commands, and take care of the dogs' needs.
- Dr. Leticia Fanucchi, an OSU associate professor and behavior management psychologist, plays a crucial role in the program, providing behavior management support.
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Medium
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182
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Battle of the TOP — LLama 3, Claude 3, GPT4 Omni, Gemini 1.5 Pro-Light and more
- LLama 3, Claude 3, GPT4 Omni, Gemini 1.5 Pro-Light, and more are the top models in the battle.
- Gemini 1.5 and GPT 4 Omni stand out for their ability to process audio and video.
- GPT 4 Omni has all the modalities, but they are not available in their API yet.
- LLama 3, Claude 3, Gemini 1.5 Flash are the models with the best performance/cost prices.
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