Mohit Pateriya’s Post

🔍 An Introduction to 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗖𝗡𝗡𝘀) 𝗪𝗵𝘆 𝗮𝗿𝗲 𝗖𝗡𝗡𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁? Convolutional Neural Networks (CNNs) are a groundbreaking innovation in artificial intelligence, powering cutting-edge solutions in computer vision. From recognizing faces in photos to enabling self-driving cars, CNNs are at the core of applications like 𝗼𝗯𝗷𝗲𝗰𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻, 𝗶𝗺𝗮𝗴𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗼𝗽𝘁𝗶𝗰𝗮𝗹 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 (𝗢𝗖𝗥). The revival of deep learning owes much to CNNs, whose success in the 𝗜𝗺𝗮𝗴𝗲𝗡𝗲𝘁 challenge reignited interest in AI research after a lull in the 1990s. 𝗛𝗼𝘄 𝗱𝗼 𝗖𝗡𝗡𝘀 𝘄𝗼𝗿𝗸? At their heart, CNNs utilize convolution and pooling operations to process and understand images. 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: A small matrix (called a kernel) slides over the input image, performing 𝗲𝗹𝗲𝗺𝗲𝗻𝘁-𝘄𝗶𝘀𝗲 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘀𝘂𝗺𝗺𝗮𝘁𝗶𝗼𝗻 to extract key features like edges and patterns. 𝗣𝗼𝗼𝗹𝗶𝗻𝗴: This step reduces the spatial dimensions of the data while retaining key information. For example, 𝗺𝗮𝘅 𝗽𝗼𝗼𝗹𝗶𝗻𝗴 selects the maximum value in a region, while 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗽𝗼𝗼𝗹𝗶𝗻𝗴 computes the mean. 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗜𝗻𝗽𝘂𝘁 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗶𝗻 𝗖𝗡𝗡𝘀 As the kernel slides over an image, the output size naturally becomes smaller due to the reduction in spatial dimensions. To address this, 𝗽𝗮𝗱𝗱𝗶𝗻𝗴 is often applied by adding zeros around the edges of the input image. This ensures that the output retains the same size as the input. CNNs have not just revolutionized AI but have also redefined how machines perceive the world. As their applications expand, so does their transformative impact across industries! #CNNs #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #MachineLearning

.🔍 What are GANs?Generative Adversarial Networks consist of two neural networks: the generator and the discriminator.The generator creates synthetic data that mimics real-world data. The discriminator evaluates this synthetic data against authentic samples to determine its validity. This adversarial training process drives both networks to improve continuously, resulting in the generation of highly realistic outputs.🎨 Key Applications of GANs in Deep LearningImage Generation: GANs are widely used for creating stunning images, including realistic human faces and intricate landscapes. Data Augmentation: By generating additional training samples, GANs enhance datasets, leading to improved model performance in machine learning tasks. Super Resolution: GANs can upscale low-resolution images, enhancing clarity and detail for better visual quality. Style Transfer: With GANs, transforming images into various artistic styles has become more accessible and effective. 🚀 Advanced Variants of GANsInnovation in GANs continues with advanced variants such as:Conditional GANs (cGANs): Allow for controlled generation based on specific conditions or labels. CycleGAN: Facilitates image-to-image translation without requiring paired examples, making it ideal for style transfer tasks. Wasserstein GAN (WGAN): Introduces a new loss function that enhances training stability and improves convergence. ⚠️ Challenges in Using GANsDespite their power, GANs face several challenges:Mode Collapse: The generator may produce a limited variety of outputs, failing to capture the full diversity of the training dataset. Training Instability: Balancing the training of both networks can be complex, often leading to convergence issues. 💡 The Future of GANsAs research progresses, the potential applications for GANs are vast. From art generation to enhancing medical imaging, the possibilities are endless!Let’s embrace this exciting technology and explore how it can revolutionize our industries!#DeepLearning #GenerativeAdversarialNetworks #MachineLearning #AI #ImageGeneration #DataAugmentation #Innovation #TechTrends

*Autoencoders: Introduction & Application use-cases* Imagine we have a set of inputs, X = { x_3, x_4, x_5, …}. Each unique item in this set can be represented by a unique identifier, Z = { z_3, z_4, z_5, …}. The process of mapping from the original data X to these identifiers is called #encoding, while the reverse process—translating the identifiers back into their original form—is known as #decoding. This concept is relatively straightforward when the input space is small, but what happens when we have a *very large input space*? Do we manually create all the possible Z-representations ourselves? The answer is no! This is where #NeuralNetworks come into play. Specifically, a unique neural network architecture called the #autoencoder is designed to handle both encoding and decoding efficiently. In the following article, we explore the concept of autoencoders and how they are applied to real-world problems today. From image reconstruction to data compression, autoencoders have a wide range of applications in modern AI. Read the full article here: https://lnkd.in/d7HRF4Ta #AI #Rectlabs #NeuralNetworks #GenerativeAI #DeepLearning #Encoders #Decoders #Transformers #ImageGeneration #ImageReconstruction #Maths

Shanghai G-Pulse Electronics Technology Company Limited, a subsidiary of #IntronTechnology released a centralized millimeter-wave radar AI perception algorithm solution based on the Horizon Journey®6 intelligent driving platform. It uses advanced AI algorithms to enhance the performance of the sensing system, and further promotes the precise detection and integration of radar and scenarios, bringing a whole new experience to intelligent driving. Based on the Horizon Journey®6 E/M intelligent driving platform and the centralized radar perception system, Intron Technology uses data as the driving force to maximize the collection of effective information, and builds a deep learning computing model with neural network algorithms as the core and continuously optimizes it. 👉Click this link for more details: https://lnkd.in/gHExRVJS #AutomotiveInnovation #RadarTechnology #SmartDriving #IntronTechnology #AIPerceptionAlgorithmSolution #HorizonJourney6 #IntelligentDriving

AI and Machine Learning in GeoDict – The future of automation in data analysis   Artificial Intelligence (AI) and Machine Learning (ML) are revolutionary technologies reshaping our interaction with data. Within GeoDict, these technologies not only streamline the material development process, but also significantly enhance its effectiveness. Here's how GeoDict's AI capabilities benefit our clients: AI features implemented in GeoDict: - Transform 3D gray-value images into digital representations of the material - Enhance image quality of 3D gray-value images - Automatically distinguish fibers, binder, and pore space - Train your own neural networks to tackle diverse challenges, like identifying contact areas between individual grains in rocks or battery electrodes (and more!) These features lead to accelerated development cycles and cost savings by minimizing resource expenditure. The capabilities of GeoDict in microstructure generation to train neural networks are virtually limitless! Interested? Follow us and learn more at: https://lnkd.in/ewqbXDuK   #ArtificialIntelligence #MachineLearning #MaterialScience #DigitalTransformation

♻️ Prompt Engineering is an emerging field that focuses on developing, designing, and optimiZing prompts to enhance the output of LLM for your needs. it gives you a way to guide the model's behaviour to the outcomes you want to achieve. FMs use deep neural networks to emulate human brain functionality and handle complex tasks. generative AI is powered by large models, commonly called foundation models. FMs are pretrained on a vast corpus of data, usually through self-supervised learning. you can adapt them for a broad range of general tasks. such as..¯⁠\⁠_⁠(⁠ ͡⁠°⁠ ͜⁠ʖ⁠ ͡⁠°⁠)⁠_⁠/⁠¯ 🌽 Text generation 🌽 Text summarisation 🌽 Information extraction 🌽 Image generation 🌽 Chatboat 🌽 Question answering. Example of FMs include.. 🧬 Amazon Titan 🧬 Meta Llama 2 (https://lnkd.in/dsJPG246) 🧬 Anthropic Claude 🧬 A121 Labs Jurassic-2, Ultra, and more. "Enter your prompt on text field and run your prompt" #AWS_Skill_Builder #Happy_Learning #LLMs #Prompt_Engineering

🚨 Unlocking the Power of Neural Networks: A Dive into Their Subfields and Real-World Applications 👀👇 Here's a breakdown of the uses for each specific neural network type, with more details but concise: ▶️ Radial Basis Function Networks (RBFNs): Predict non-linear trends like stock prices or weather patterns. ▶️ Recurrent Neural Networks (RNNs): Powering chatbots, machine translation, and automatic captioning by understanding sequences in text, speech, or even video. ▶️ Autoencoders: Compress images and videos, making them smaller for storage or transmission, while also being able to detect unusual data in sensor readings. ▶️ Hopfield Networks: Act like a supercharged associative memory, helping you find similar images or data patterns. ▶️Modular Neural Networks: Break down complex tasks into smaller, manageable pieces. Imagine a robot that uses separate neural networks for vision, movement, and object recognition – that's modular networks at work! ▶️ Adaptive Resonance Theory (ART): Automatically categorize data, useful for image segmentation or grouping customer data by purchase history. Are you looking for support in your digital transformation execution? Experts can help to adapt and integrate new tools to face competition and market. Follow BC Impact! Hugo Jacques 🛡 #AI #ML #digitalinitiative #digitaltransformation #insurance #bank #Fund #finance

🤖 ** Generative Adversarial Networks ** Did you know that some of the most stunning AI-generated images and even realistic deepfakes are created using a technology called “Generative Adversarial Networks” (GANs)? Here’s how they work: GANs consist of two neural networks—a **generator** and a **discriminator**—that compete against each other. The generator creates fake data (like images), while the discriminator tries to determine whether the data is real or generated. Over time, this competition makes the generator so good that it can create highly realistic images, audio, or even video. Fun fact: GANs are the technology behind AI-generated art, photorealistic images, and even the synthetic faces you see online that don’t belong to real people! This breakthrough has incredible applications in creative industries, data augmentation, and even training AI models with synthetic data. #AI #MachineLearning #GANs #DeepLearning #TechInnovation #AIArt #DataScience

The ultimate upgrade in vision technology has arrived. If you're in this industry, you can't miss this. It's already here ... and has been for a while. #AI Machine Vision is one of the first areas where AI was applied effectively in industrial automation. The real shift towards AI in machine vision started in the 1980s with the application of simple neural networks. These networks could process image data for pattern recognition, albeit at a basic level initially. Over the years, the advent of more complex algorithms and deep learning has significantly advanced machine vision capabilities. Convolutional Neural Networks (CNNs) are particularly pivotal in this field, allowing for highly sophisticated image analysis and interpretation. Today, I'm proud to be working for a company that has some of the finest engineers in this field. There's a good chance you won't even know our algorithms are working in the background. I am constantly in awe every time our Genii release another software update. Donato Montanari Mateusz Barteczko Pierantonio Boriero Zebra Technologies Adaptive Vision. #machinevision #industrialautomation

LauRel: Learned Augmented Residular Layer Think about neural networks: they essentially stack a series of non-linearities between the input and the output. This holds true for convolutional blocks (transforming raw pixels into low-level features, which feed into higher-level features) and transformer blocks (processing raw text into low-level features like parts of speech, leading to higher-level concepts). In short: input → bunch of non-linearities → output. While powerful, relying on non-linearities alone has its downsides—it can make models harder to converge and increase their overall footprint. But what if we focused on learning from linearities instead? input → bunch of linearities → output This idea underpins residual connections, which have become a standard in modern architectures. They work by combining the output of the previous layer with the current one, enabling better training dynamics and faster convergence. Now, LAUREL takes this concept even further. Instead of merely combining the output of the previous layer with the current layer, it learns how to aggregate outputs across all layers dynamically. The result? Higher accuracy with a smaller model footprint. This approach shows exciting potential for advancing neural network design https://lnkd.in/gxj95mqQ #slms #ai #laurel #efficiency