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Deep Learning News
Medium
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Vision Transformers: Theory and Practical Implementation from Scratch
- In this article, we’ll dive into the theory behind vision transformers, understand why they’re becoming increasingly popular, compare them with CNNs, and discuss when to use them.
- Transformers lack the inherent ability to understand the position of image patches. To compensate for this, positional encodings are added to the patch embeddings, enabling the model to learn the relative positions of patches in an image.
- The strength of vision transformers lies in their ability to model global dependencies and context across the entire image, making them especially effective for large-scale datasets.
- Traditional deep learning models for computer vision — like CNNs — excel at extracting spatial features through convolution operations.
- Vision transformers break this paradigm. Instead of convolutions, they rely on a self-attention mechanism that allows the model to look at the entire image globally, learning relationships between patches regardless of their distance.
- CNNs are ideal for tasks requiring local feature extraction and fast training, while ViTs shine when global context and scalability are key.
- In this section, we’ll explore how to build a vision transformer from scratch to gain a deeper understanding of its architecture and how each component works together.
- Now, let’s take a look at how to use pretrained Vision Transformers (ViTs) with the help of the transformers library from Hugging Face.
- The ViTImageProcessor handles image resizing and normalization according to the model’s requirements.
- ViTs are poised to enhance the performance of AI systems in visual data interpretation and beyond.
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Medium
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Generalization: Understanding CS229( Generalization and Regularization)
- The test error is the ultimate measure of how well the model generalizes.
- Generalization bounds establish bounds that relate the training error to the test error.
- The "double descent" phenomenon suggests that further scaling in model size may improve generalization.
- Regularization techniques like adding penalty terms can help mitigate overfitting and improve generalization.
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Medium
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Do you know about Character AI (c.ai)?
- c.ai characters engage in a surprisingly real way, making it easy to forget they're AI.
- There are multiple ways to use c.ai, from exploring ideas to learning new things or just chatting for fun.
- The characters in c.ai show empathy and understanding, which is quite astonishing.
- Character AI showcases the advancements in AI and leaves a lasting impression on users.
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Medium
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Understanding Dense Neural Networks: A Deep Dive into Architecture, Learning, and Applications
- Dense neural networks are essential for compressing information from higher-dimensional data into structured outputs.
- Hidden layers in DNNs are fully connected layers between the input and output layers.
- Dense layers use activation functions to introduce non-linearities into the model, enabling it to capture complex patterns.
- DNNs are commonly used for classification in structured data, regression tasks, reinforcement learning, and as final dense layers in NLP architectures.
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Analyticsindiamag
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This New Logic Gate Network Reduces Inference Speed to Only 4 Nanoseconds
- Researchers at Stanford University have introduced convolutional differentiable logic gate networks (LGNs) with logic gate tree kernels.
- The research combines concepts from machine vision with differentiable logic gate networks, allowing for the training of deeper LGNs and improving training efficiency.
- The proposed architecture, 'LogicTreeNet', decreases model size and improves accuracy compared to the state of the art.
- The model achieves inference speeds of only 4 nanoseconds and improves accuracy on MNIST and CIFAR-10 datasets.
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Marktechpost
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FinSafeNet: Advancing Digital Banking Security with Deep Learning for Fraud Detection and Real-Time Transaction Protection
- With rapid technological advances and increased internet use in business, cybersecurity has become a major global concern, especially in digital banking and payments.
- Researchers have developed FinSafeNet, a deep-learning model for secure digital banking, which achieved 97.8% accuracy in fraud detection on the Paysim database.
- FinSafeNet incorporates advanced features such as Bi-LSTM, CNN, dual attention mechanism, and optimized feature selection using the Improved Snow-Lion Optimization Algorithm (I-SLOA).
- The model offers potential for real-time deployment in diverse banking environments, and future blockchain integration could further reinforce transaction security against cyber threats.
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Medium
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Groundbreaking AI Innovations That Are Transforming Business Processes Forever
- AI agents are transforming the way businesses operate and offering unprecedented efficiency to business processes.
- AI is not just a tool but a strategic partner, offering smart solutions to relieve stress and reclaim precious time.
- AI excels in data-pooling and predictive analysis, helping businesses make use of pattern recognition and interpret patterns to make routine processes smoother.
- AI enhances human input by automating mundane tasks, freeing up time and allowing for greater focus on creative problem-solving.
- AI is a versatile tool for customer service, providing tireless assistance to human reps and answering questions with boundless patience.
- AI offers insights and acts like a trusted advisor when working alongside managers in various fields.
- AI has the ability to learn and adapt continuously from every interaction, extending its role from a static tool to a dynamic ally.
- A reliable and tailored strategy, combined with modular AI platforms like scheduling AI, maximizes personal productivity and achieves success.
- AI has become more than just a buzzword and has transformed into a linchpin of modern business strategies for tech giants and other entrepreneurial ventures.
- AI has shifted the landscape by reducing operational bottlenecks, increasing efficiency and innovation more than expected.
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Nvidia
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Japan Develops Next-Generation Drug Design, Healthcare Robotics and Digital Health Platforms
- Japan is using AI tools to provide high-quality healthcare to its population of around 30% people who are 65 or older. Breakthrough technology deployments by the country’s healthcare leaders including in AI-accelerated drug discovery, genomic medicine, healthcare imaging, and robotics are highlighted at the NVIDIA AI Summit Japan. AI scanners and scopes give radiologists and surgeons real-time superpowers. Japanese surgical AI companies are investigating the use of Holoscan to power applications that could detect anatomical structures like organs in real-time. Fujifilm has launched NURA, a group of health screening centers with AI-augmented medical examinations designed to help doctors test for cancer and chronic diseases. Developed using NVIDIA DGX systems, the tool incorporates large language models that create text summaries of medical images.
- AI tools trained on country-specific data and local compute infrastructure are supercharging the abilities of Japan’s clinicians and researchers amid an expected shortage of nearly 500,000 healthcare workers by next year.
- Powered by NVIDIA AI computing platforms like the Tokyo-1 NVIDIA DGX supercomputer, these applications were developed using domain-specific platforms.
- NVIDIA is supporting Japan’s pharmaceutical market with NVIDIA BioNeMo, an end-to-end platform that enables drug discovery researchers to develop and deploy AI models for generating biological intelligence from biomolecular data.
- Tokyo-based Astellas Pharma uses BioNeMo biomolecular AI models to accelerate biologics research. Astellas has accelerated chemical molecule generation by more than 30x. The company plans to use BioNeMo NIM microservices to further advance its work.
- Genomics researchers across Japan have adopted the NVIDIA Parabricks software suite to accelerate secondary analysis of DNA and RNA data. The University of Tokyo Human Genome Center uses it to find gene variants unique to Japan’s population.
- Fujifilm has developed an AI application in collaboration with NVIDIA to help surgeons perform surgery more efficiently by converting CT images into 3D simulations to support surgery.
- Olympus recently collaborated with NVIDIA and NTT to demonstrate how cloud-connected endoscopes can efficiently run image processing and AI applications in real-time.
- NVIDIA is also supporting real-time AI-powered robotic systems for radiology and surgery in Japan with Holoscan, a sensor processing platform that streamlines AI model and application development for real-time insights.
- Fujifilm has launched NURA, a group of health screening centers with AI-augmented medical examinations designed to help doctors test for cancer and chronic diseases with faster examinations and lower radiation doses for CT scans.
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Medium
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Paradise of Dice and the Circle of Hello
- Paradise depicts the gamblers' table of life, where we all throw odds into the hands of fate.
- Hell stands as a reminder of hidden strength, where we learn to strive, challenge ourselves, and promise that we will.
- Hell transforms into Hello, a circle of warmth, where the test becomes the doorway to new relationships.
- The cycle of Paradise, Hell, and Hello represents a beautiful dance full of courage, effort, and love.
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Medium
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5 Shocking Ways AI Agents Are Transforming Business Operations Forever
- AI agents are becoming integral to business operations, helping companies make smarter decisions, optimize processes, and engage customers in new ways.
- AI can automate repetitive tasks, such as sorting through vast amounts of customer data, allowing human teams to focus on the more creative aspects of the work.
- AI agents are capable of answering customer inquiries 24/7 with unparalleled accuracy, anticipating needs based on prior interactions and preferences.
- AI can predict customer behavior patterns, future trends, and even craft personalized marketing campaigns.
- Focusing on key Artificial Intelligence applications delivered immediate value without overwhelming team or resources. By starting small and scaling strategically, many pitfalls can be avoided.
- Security was a primary concern, but by working with trusted AI providers, customer data remained protected.
- AI agents don’t replace jobs; it transforms them. The workforce can be shifted from mundane tasks to more strategic and creative roles, enhancing job satisfaction.
- AI agents are practical tools reshaping business operations today, crafting a new business era.
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Medium
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9 PyTorch Layers You Must learn it
- torch.nn.Linear: Applies a linear transformation to incoming data by multiplying the input with a weight matrix and adding a bias.
- Convolutional Layer: Applies convolutional filters to input data to extract spatial or temporal patterns.
- Recurrent Layers: Used to handle sequential data by keeping information over time and learning temporal dependencies.
- Embedding Layer: Converts input indices into dense vectors of fixed size, commonly used for representing discrete items.
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Medium
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Narcissistic and Evil Mothers
- The author reflects on the painful experiences of growing up with a narcissistic and evil mother.
- The author describes being manipulated and controlled by their mother, who would argue and take away gifts when they went out with friends, only to give them back later.
- The author recounts a shocking incident where their mother accused them of planning to rape her during an argument.
- The author expresses feelings of being a monster due to the toxic environment created by their parents and the emotional abuse endured throughout their childhood.
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Medium
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Transformers (Cross Attention)
- Cross Attention, a multi-head attention block, receives inputs from both the encoder and decoder, unlike other multi-head attention blocks. Cross Attention allows finding the relationship between two sequences, which is the most important component of the decoder architecture of Transformers.
- The Cross Attention mechanism helps to capture the relationship between each word of the input sequence and each word of the output sequence. It involves finding the relationship between two different sequences by multi-head attention.
- Cross Attention is used in situations where two different types of sequences need to be compared. For instance, machine translation, question-answering systems, and multimodality applications such as image captioning, text-to-image, text-to-speech.
- Query vectors come from the output sequence while Key and Value vectors come from the input sequence. The calculation for attention scores in Cross Attention is same as in Self Attention. Both mechanisms are similar except Cross Attention deals with two sequences instead of one.
- The difference between the self attention and cross attention lies in the input aspect(the input for self-attention is the embeddings of a sequence while Cross Attention simultaneously requires two sequences in the input) and output aspect (the number of word vectors in the contextual embeddings obtained from Cross Attention is equal to the number of words in the output sequence).
- The structure of Cross Attention is conceptually very similar to that of self-attention, except for its input and output differences, and with the addition of the other sequence in the processing.
- Cross Attention is used in situations where two different types of sequences need to be compared. For instance, machine translation, question-answering systems, and multimodality applications such as image captioning, text-to-image, text-to-speech.
- The Cross Attention mechanism is frequently used in situations that requiresimultaneously check for the relationships between two sequences.
- Cross Attention mechanism of the Decoder architecture of transformers is essential and a high-level concept that needs to be understood for building NLP applications, and for understanding the working of transformer applications.
- Understanding Cross Attention requires understanding of Self Attention, the decoding architecture of the transformer, and the use case scenarios to finally grasp the concept.
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Medium
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Some Specific Research topics focusing on Self-attention and Transformers:
- Develop methods to reduce the computational complexity of transformers for handling large datasets.
- Explore the use of self-attention mechanisms in models that integrate text, image, and audio data.
- Study the advancements in transformer-based models like BERT, GPT, and their applications in various NLP tasks.
- Apply self-attention to graph neural networks for tasks like node classification and graph generation.
- Develop self-attention mechanisms that provide better interpretability and transparency in model predictions.
- Use self-attention mechanisms to improve the analysis and forecasting of time series data.
- Investigate the application of transformers in reinforcement learning environments to enhance decision-making processes.
- Apply self-attention mechanisms to detect anomalies in various types of data, such as network traffic or financial transactions.
- Study the role of cross-attention mechanisms in improving the performance of multi-task learning models.
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Arstechnica
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How a stubborn computer scientist accidentally launched the deep learning boom
- In 2008, neural networks were considered a backwater in the field of artificial intelligence.
- Researchers had shifted their focus to support vector machines and other approaches.
- Meanwhile, a team at Princeton led by Prof. Fei-Fei Li was working on a project that would revolutionize neural networks.
- Their work would eventually lead to the deep learning boom.
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