๐ How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition
๐ Category: MACHINE LEARNING
๐ Date: 2025-11-18 | โฑ๏ธ Read time: 14 min read
Automatic plant leaf recognition leverages deep feature embeddings to transform leaf images into dense numerical vectors in a high-dimensional space. By calculating the Euclidean similarity between these vector representations, machine learning models can accurately identify and classify plant species. This computer vision technique provides a powerful and scalable solution for botanical and agricultural applications, moving beyond traditional manual identification methods.
#ComputerVision #MachineLearning #DeepLearning #FeatureEmbeddings #ImageRecognition
๐ Category: MACHINE LEARNING
๐ Date: 2025-11-18 | โฑ๏ธ Read time: 14 min read
Automatic plant leaf recognition leverages deep feature embeddings to transform leaf images into dense numerical vectors in a high-dimensional space. By calculating the Euclidean similarity between these vector representations, machine learning models can accurately identify and classify plant species. This computer vision technique provides a powerful and scalable solution for botanical and agricultural applications, moving beyond traditional manual identification methods.
#ComputerVision #MachineLearning #DeepLearning #FeatureEmbeddings #ImageRecognition
โค1
๐ The Machine Learning and Deep Learning โAdvent Calendarโ Series: The Blueprint
๐ Category: MACHINE LEARNING
๐ Date: 2025-11-30 | โฑ๏ธ Read time: 7 min read
A new "Advent Calendar" series demystifies Machine Learning and Deep Learning. Follow a step-by-step blueprint to understand the inner workings of complex models directly within Microsoft Excel, effectively opening the "black box" for a hands-on learning experience.
#MachineLearning #DeepLearning #Excel #DataScience
๐ Category: MACHINE LEARNING
๐ Date: 2025-11-30 | โฑ๏ธ Read time: 7 min read
A new "Advent Calendar" series demystifies Machine Learning and Deep Learning. Follow a step-by-step blueprint to understand the inner workings of complex models directly within Microsoft Excel, effectively opening the "black box" for a hands-on learning experience.
#MachineLearning #DeepLearning #Excel #DataScience
โค1
๐ Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch
๐ Category: DEEP LEARNING
๐ Date: 2025-12-03 | โฑ๏ธ Read time: 10 min read
Unlock peak PyTorch performance by addressing the hidden bottlenecks caused by variable-shaped tensors. This deep dive focuses on the critical data sampling phase, offering practical optimization strategies to handle tensors of varying sizes efficiently. Learn how to analyze and improve your data loading pipeline for faster model training and overall performance gains.
#PyTorch #PerformanceOptimization #DeepLearning #MLOps
๐ Category: DEEP LEARNING
๐ Date: 2025-12-03 | โฑ๏ธ Read time: 10 min read
Unlock peak PyTorch performance by addressing the hidden bottlenecks caused by variable-shaped tensors. This deep dive focuses on the critical data sampling phase, offering practical optimization strategies to handle tensors of varying sizes efficiently. Learn how to analyze and improve your data loading pipeline for faster model training and overall performance gains.
#PyTorch #PerformanceOptimization #DeepLearning #MLOps
โค4
๐ On the Challenge of Converting TensorFlow Models to PyTorch
๐ Category: DEEP LEARNING
๐ Date: 2025-12-05 | โฑ๏ธ Read time: 19 min read
Converting legacy TensorFlow models to PyTorch presents significant challenges but offers opportunities for modernization and optimization. This guide explores the common hurdles in the migration process, from architectural differences to API incompatibilities, and provides practical strategies for successfully upgrading your AI/ML pipelines. Learn how to not only convert but also enhance your models for better performance and maintainability in the PyTorch ecosystem.
#PyTorch #TensorFlow #ModelConversion #MLOps #DeepLearning
๐ Category: DEEP LEARNING
๐ Date: 2025-12-05 | โฑ๏ธ Read time: 19 min read
Converting legacy TensorFlow models to PyTorch presents significant challenges but offers opportunities for modernization and optimization. This guide explores the common hurdles in the migration process, from architectural differences to API incompatibilities, and provides practical strategies for successfully upgrading your AI/ML pipelines. Learn how to not only convert but also enhance your models for better performance and maintainability in the PyTorch ecosystem.
#PyTorch #TensorFlow #ModelConversion #MLOps #DeepLearning
โค4
Forwarded from Machine Learning with Python
โก๏ธ All cheat sheets for programmers in one place.
There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks.
No registration required and it's free.
https://overapi.com/
#python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
https://xn--r1a.website/CodeProgrammerโก๏ธ
There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks.
No registration required and it's free.
https://overapi.com/
#python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
https://xn--r1a.website/CodeProgrammer
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โค7
Forwarded from Machine Learning with Python
DS Interview.pdf
1.6 MB
Data Science Interview questions
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
https://xn--r1a.website/CodeProgrammer
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
https://xn--r1a.website/CodeProgrammer
๐2โค1
Forwarded from Machine Learning with Python
๐ A fresh deep learning course from MIT is now publicly available
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
โก๏ธ Link to the course
tags: #Python #DataScience #DeepLearning #AI
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
โก๏ธ Link to the course
tags: #Python #DataScience #DeepLearning #AI
โค2
Forwarded from AI & ML Papers
Exploring the Future of AI: Neutrosophic Graph Neural Networks (NGNN)
Recent analysis indicates that Neutrosophic Graph Neural Networks (NGNN) represent a significant advancement in contemporary artificial intelligence research. The following overview details the concept and its implications.
Most artificial intelligence models presuppose data integrity; however, real-world data is frequently imperfect. Consequently, NGNN may emerge as a critical innovation.
The foundational inquiry addresses the following:
How does artificial intelligence manage data characterized by uncertainty, incompleteness, or contradiction?
Traditional models exhibit limitations in this regard, often assuming certainty where none exists.
The Foundation: Neutrosophic Logic
In the late 1990s, mathematician Florentin Smarandache introduced a framework extending beyond binary true/false dichotomies. He proposed three dimensions of truth:
T โ What is true
I โ What is indeterminate
F โ What is false
Between 2000 and 2015, this framework evolved into neutrosophic sets and neutrosophic graphs, mathematical tools capable of encoding uncertainty within data and relationships.
The Parallel Rise of Graph Neural Networks
Around 2016, the artificial intelligence sector adopted Graph Neural Networks (GNNs), models designed to learn from nodes (data points) and edges (relationships). These models became foundational in social networks, healthcare, fraud detection, and bioinformatics.
However, GNNs possess a critical limitation: they assume data certainty, whereas real-world data is inherently uncertain.
The Convergence: NGNN
From 2020 onwards, researchers began integrating these two domains. In an NGNN, rather than carrying only features, a node encapsulates:
โ T: What is likely true
โ I: What remains uncertain
โ F: What may be false
This constitutes not a minor upgrade, but a fundamental shift in how artificial intelligence models perceive and process reality.
Key Application Areas:
Healthcare โ Navigating uncertain or conflicting diagnoses
Fraud detection โ Identifying ambiguous behavioral patterns
Social networks โ Modeling unclear or evolving relationships
Bioinformatics โ Managing the complexity of biological interactions
Is NGNN advanced machine learning?
Affirmatively. It resides at the intersection of:
Graph theory ยท Deep learning ยท Mathematical logic ยท Uncertainty modeling
This technology represents research-level, cutting-edge development and is not yet widely deployed in industry. This status underscores its current strategic importance.
The Broader Context
NGNN is not merely another model; it signifies a philosophical shift in artificial intelligence from systems assuming certainty to systems reasoning through uncertainty. Real-world problems are rarely perfect; therefore, models should not presume perfection.
This represents not only evolution but a definitive direction for the field.
โโ
#ArtificialIntelligence #MachineLearning #DeepLearning #GraphNeuralNetworks #AIResearch #DataScience #FutureOfAI #Innovation #EmergingTech #NGNN #AIHealthcare #Bioinformatics
Recent analysis indicates that Neutrosophic Graph Neural Networks (NGNN) represent a significant advancement in contemporary artificial intelligence research. The following overview details the concept and its implications.
Most artificial intelligence models presuppose data integrity; however, real-world data is frequently imperfect. Consequently, NGNN may emerge as a critical innovation.
The foundational inquiry addresses the following:
How does artificial intelligence manage data characterized by uncertainty, incompleteness, or contradiction?
Traditional models exhibit limitations in this regard, often assuming certainty where none exists.
The Foundation: Neutrosophic Logic
In the late 1990s, mathematician Florentin Smarandache introduced a framework extending beyond binary true/false dichotomies. He proposed three dimensions of truth:
T โ What is true
I โ What is indeterminate
F โ What is false
Between 2000 and 2015, this framework evolved into neutrosophic sets and neutrosophic graphs, mathematical tools capable of encoding uncertainty within data and relationships.
The Parallel Rise of Graph Neural Networks
Around 2016, the artificial intelligence sector adopted Graph Neural Networks (GNNs), models designed to learn from nodes (data points) and edges (relationships). These models became foundational in social networks, healthcare, fraud detection, and bioinformatics.
However, GNNs possess a critical limitation: they assume data certainty, whereas real-world data is inherently uncertain.
The Convergence: NGNN
From 2020 onwards, researchers began integrating these two domains. In an NGNN, rather than carrying only features, a node encapsulates:
โ T: What is likely true
โ I: What remains uncertain
โ F: What may be false
This constitutes not a minor upgrade, but a fundamental shift in how artificial intelligence models perceive and process reality.
Key Application Areas:
Healthcare โ Navigating uncertain or conflicting diagnoses
Fraud detection โ Identifying ambiguous behavioral patterns
Social networks โ Modeling unclear or evolving relationships
Bioinformatics โ Managing the complexity of biological interactions
Is NGNN advanced machine learning?
Affirmatively. It resides at the intersection of:
Graph theory ยท Deep learning ยท Mathematical logic ยท Uncertainty modeling
This technology represents research-level, cutting-edge development and is not yet widely deployed in industry. This status underscores its current strategic importance.
The Broader Context
NGNN is not merely another model; it signifies a philosophical shift in artificial intelligence from systems assuming certainty to systems reasoning through uncertainty. Real-world problems are rarely perfect; therefore, models should not presume perfection.
This represents not only evolution but a definitive direction for the field.
โโ
#ArtificialIntelligence #MachineLearning #DeepLearning #GraphNeuralNetworks #AIResearch #DataScience #FutureOfAI #Innovation #EmergingTech #NGNN #AIHealthcare #Bioinformatics
โค1
๐ Why Modern AI Runs on GPUs and TPUs Instead of CPUs ๐ค
AI models are essentially large matrix multiplication engines ๐งฎ.
Training and inference involve billions or even trillions of tensor operations like:
๐ [Input Tensor] ร [Weight Matrix] = Output โก๏ธ
The speed of these computations depends heavily on the hardware architecture ๐.
Traditional CPUs execute operations sequentially โณ. A few powerful cores handle tasks one after another. This design is excellent for general purpose computing but inefficient for massive tensor workloads ๐ข.
Example:
A transformer model performing attention calculations may require billions of multiplications. A CPU processes them sequentially which increases latency ๐.
๐ GPUs solve this with parallelism ๐
GPUs contain thousands of smaller cores designed to execute many matrix operations simultaneously. Instead of one operation at a time, thousands run in parallel ๐.
Example:
Training a CNN for image classification:
- CPU training time โ several hours โฐ
- GPU training time โ minutes โก๏ธ
Frameworks like PyTorch and TensorFlow leverage CUDA cores to parallelize tensor computations across thousands of threads ๐ง.
๐ TPUs go even further ๐ธ
TPUs are purpose built accelerators for deep learning workloads. They use systolic array architecture optimized for dense matrix multiplication ๐.
Instead of sending data back and forth between memory and compute units, data flows directly through a grid of processing elements ๐.
Example:
Large language models like BERT or PaLM run inference much faster on TPUs due to optimized tensor pipelines ๐.
Typical latency differences โฑ๏ธ
CPU โ Seconds
GPU โ Milliseconds
TPU โ Microseconds
As models scale to billions of parameters, hardware architecture becomes the real bottleneck ๐ง.
That is why modern AI infrastructure relies on GPU clusters and TPU pods to train and serve large models efficiently ๐ข.
๐กKey takeaway
AI progress is not only about better algorithms ๐ง . It is also about better compute architecture ๐.
#AI #MachineLearning #DeepLearning #GPUs #TPUs #LLM #DataScience
#ArtificialIntelligence
AI models are essentially large matrix multiplication engines ๐งฎ.
Training and inference involve billions or even trillions of tensor operations like:
๐ [Input Tensor] ร [Weight Matrix] = Output โก๏ธ
The speed of these computations depends heavily on the hardware architecture ๐.
Traditional CPUs execute operations sequentially โณ. A few powerful cores handle tasks one after another. This design is excellent for general purpose computing but inefficient for massive tensor workloads ๐ข.
Example:
A transformer model performing attention calculations may require billions of multiplications. A CPU processes them sequentially which increases latency ๐.
๐ GPUs solve this with parallelism ๐
GPUs contain thousands of smaller cores designed to execute many matrix operations simultaneously. Instead of one operation at a time, thousands run in parallel ๐.
Example:
Training a CNN for image classification:
- CPU training time โ several hours โฐ
- GPU training time โ minutes โก๏ธ
Frameworks like PyTorch and TensorFlow leverage CUDA cores to parallelize tensor computations across thousands of threads ๐ง.
๐ TPUs go even further ๐ธ
TPUs are purpose built accelerators for deep learning workloads. They use systolic array architecture optimized for dense matrix multiplication ๐.
Instead of sending data back and forth between memory and compute units, data flows directly through a grid of processing elements ๐.
Example:
Large language models like BERT or PaLM run inference much faster on TPUs due to optimized tensor pipelines ๐.
Typical latency differences โฑ๏ธ
CPU โ Seconds
GPU โ Milliseconds
TPU โ Microseconds
As models scale to billions of parameters, hardware architecture becomes the real bottleneck ๐ง.
That is why modern AI infrastructure relies on GPU clusters and TPU pods to train and serve large models efficiently ๐ข.
๐กKey takeaway
AI progress is not only about better algorithms ๐ง . It is also about better compute architecture ๐.
#AI #MachineLearning #DeepLearning #GPUs #TPUs #LLM #DataScience
#ArtificialIntelligence
โค4
๐งฌ ๐๐๐ ๐๐ ๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐ โ ๐๐๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐ (๐๐๐๐ฌ)
CNNs are a class of deep neural networks designed specifically for processing grid-like data, such as images. They automatically learn spatial hierarchies of features using convolution operations, moving from simple edges to complex object recognition. ๐ง ๐ผ๐
๐. ๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐ & ๐๐๐๐๐ ๐๐๐
The strength of a CNN lies in its structured approach to feature extraction and classification. โ๏ธโจ
๐ฅ ๐๐ง๐ฉ๐ฎ๐ญ ๐๐๐ฒ๐๐ซ: Raw image pixels are fed into the network.
๐งฉ ๐๐จ๐ง๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง ๐๐๐ฒ๐๐ซ: Filters slide over the image to detect spatial patterns.
๐ ๐๐จ๐จ๐ฅ๐ข๐ง๐ ๐๐๐ฒ๐๐ซ: Reduces spatial dimensions while preserving the most critical features through Max or Average pooling.
๐ง ๐ ๐ฎ๐ฅ๐ฅ๐ฒ ๐๐จ๐ง๐ง๐๐๐ญ๐๐ ๐๐๐ฒ๐๐ซ: Combines all learned features to make a final decision.
๐. ๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐๐๐๐
What makes CNNs unique compared to standard ANNs? ๐ค๐
๐ ๐๐จ๐๐๐ฅ ๐๐จ๐ง๐ง๐๐๐ญ๐ข๐ฏ๐ข๐ญ๐ฒ: Captures specific regions of an image.
๐ ๐๐๐ข๐ ๐ก๐ญ ๐๐ก๐๐ซ๐ข๐ง๐ : Reduces the number of parameters, making the model more efficient.
๐ ๐๐ซ๐๐ง๐ฌ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ฏ๐๐ซ๐ข๐๐ง๐๐: Recognition remains accurate even if the object's position shifts slightly.
๐. ๐๐๐๐๐๐๐๐๐ ๐๐๐ ๐๐๐๐๐๐
๐ ๐๐๐ง๐๐ญ-๐: The pioneer in digit recognition.
๐ฅ ๐๐ฅ๐๐ฑ๐๐๐ญ: The 2012 model that ignited the modern deep learning revolution.
๐งฑ ๐๐๐ฌ๐๐๐ญ: Introduced \"Residual Blocks\" to allow for incredibly deep networks without losing information.
๐ ๐๐๐๐ข๐๐ข๐๐ง๐ญ๐๐๐ญ: Optimized for the best balance between speed and accuracy.
๐. ๐๐๐๐-๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐
CNNs are the silent engine behind many modern technologies: ๐๐
๐ฅ ๐๐๐๐ข๐๐๐ฅ ๐๐ฆ๐๐ ๐ข๐ง๐ : Automating the detection of anomalies in scans.
๐ ๐๐ฎ๐ญ๐จ๐ง๐จ๐ฆ๐จ๐ฎ๐ฌ ๐๐๐ก๐ข๐๐ฅ๐๐ฌ: Enabling cars to perceive their surroundings in real-time.
๐ ๐ ๐๐๐ ๐๐๐๐จ๐ ๐ง๐ข๐ญ๐ข๐จ๐ง: Powering security and authentication systems.
๐. ๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐: ๐๐๐๐๐๐๐๐๐๐๐ & ๐๐๐๐๐๐๐
๐ ๐๐จ๐ง๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง ๐๐๐ฒ๐๐ซ: Filters (kernels) slide over the input image to detect patterns like shapes and textures.
๐ ๐๐๐๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง: Introduces non-linearity, allowing the model to learn complex patterns while remaining computationally efficient.
๐ ๐๐จ๐จ๐ฅ๐ข๐ง๐ ๐๐๐ฒ๐๐ซ: Reduces spatial dimensions (Max or Average Pooling) while preserving the most important information.
๐. ๐๐๐ ๐ ๐๐๐๐ ๐๐๐๐๐: ๐ ๐๐๐ ๐ ๐๐๐๐๐๐๐ ๐๐ ๐๐๐๐๐๐๐๐
Once features are extracted, the model moves to decision-making: ๐ฏ๐ง
๐ ๐ ๐ฅ๐๐ญ๐ญ๐๐ง๐ข๐ง๐ : 2D feature maps are converted into a 1D vector.
๐งฉ ๐ ๐ฎ๐ฅ๐ฅ๐ฒ ๐๐จ๐ง๐ง๐๐๐ญ๐๐ ๐๐๐ฒ๐๐ซ: Combines learned features to perform final high-level reasoning.
๐ ๐๐จ๐๐ญ๐ฆ๐๐ฑ ๐๐๐ฒ๐๐ซ: Converts scores into probabilities for each class (e.g., Cat vs. Dog).
\"CNNs taught machines to see the worldโone filter at a time.\" ๐๐๐ค
#AI #DeepLearning #CNN #NeuralNetworks #ComputerVision #Tech
CNNs are a class of deep neural networks designed specifically for processing grid-like data, such as images. They automatically learn spatial hierarchies of features using convolution operations, moving from simple edges to complex object recognition. ๐ง ๐ผ๐
๐. ๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐ & ๐๐๐๐๐ ๐๐๐
The strength of a CNN lies in its structured approach to feature extraction and classification. โ๏ธโจ
๐ฅ ๐๐ง๐ฉ๐ฎ๐ญ ๐๐๐ฒ๐๐ซ: Raw image pixels are fed into the network.
๐งฉ ๐๐จ๐ง๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง ๐๐๐ฒ๐๐ซ: Filters slide over the image to detect spatial patterns.
๐ ๐๐จ๐จ๐ฅ๐ข๐ง๐ ๐๐๐ฒ๐๐ซ: Reduces spatial dimensions while preserving the most critical features through Max or Average pooling.
๐ง ๐ ๐ฎ๐ฅ๐ฅ๐ฒ ๐๐จ๐ง๐ง๐๐๐ญ๐๐ ๐๐๐ฒ๐๐ซ: Combines all learned features to make a final decision.
๐. ๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐๐๐๐
What makes CNNs unique compared to standard ANNs? ๐ค๐
๐ ๐๐จ๐๐๐ฅ ๐๐จ๐ง๐ง๐๐๐ญ๐ข๐ฏ๐ข๐ญ๐ฒ: Captures specific regions of an image.
๐ ๐๐๐ข๐ ๐ก๐ญ ๐๐ก๐๐ซ๐ข๐ง๐ : Reduces the number of parameters, making the model more efficient.
๐ ๐๐ซ๐๐ง๐ฌ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ฏ๐๐ซ๐ข๐๐ง๐๐: Recognition remains accurate even if the object's position shifts slightly.
๐. ๐๐๐๐๐๐๐๐๐ ๐๐๐ ๐๐๐๐๐๐
๐ ๐๐๐ง๐๐ญ-๐: The pioneer in digit recognition.
๐ฅ ๐๐ฅ๐๐ฑ๐๐๐ญ: The 2012 model that ignited the modern deep learning revolution.
๐งฑ ๐๐๐ฌ๐๐๐ญ: Introduced \"Residual Blocks\" to allow for incredibly deep networks without losing information.
๐ ๐๐๐๐ข๐๐ข๐๐ง๐ญ๐๐๐ญ: Optimized for the best balance between speed and accuracy.
๐. ๐๐๐๐-๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐
CNNs are the silent engine behind many modern technologies: ๐๐
๐ฅ ๐๐๐๐ข๐๐๐ฅ ๐๐ฆ๐๐ ๐ข๐ง๐ : Automating the detection of anomalies in scans.
๐ ๐๐ฎ๐ญ๐จ๐ง๐จ๐ฆ๐จ๐ฎ๐ฌ ๐๐๐ก๐ข๐๐ฅ๐๐ฌ: Enabling cars to perceive their surroundings in real-time.
๐ ๐ ๐๐๐ ๐๐๐๐จ๐ ๐ง๐ข๐ญ๐ข๐จ๐ง: Powering security and authentication systems.
๐. ๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐: ๐๐๐๐๐๐๐๐๐๐๐ & ๐๐๐๐๐๐๐
๐ ๐๐จ๐ง๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง ๐๐๐ฒ๐๐ซ: Filters (kernels) slide over the input image to detect patterns like shapes and textures.
๐ ๐๐๐๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง: Introduces non-linearity, allowing the model to learn complex patterns while remaining computationally efficient.
๐ ๐๐จ๐จ๐ฅ๐ข๐ง๐ ๐๐๐ฒ๐๐ซ: Reduces spatial dimensions (Max or Average Pooling) while preserving the most important information.
๐. ๐๐๐ ๐ ๐๐๐๐ ๐๐๐๐๐: ๐ ๐๐๐ ๐ ๐๐๐๐๐๐๐ ๐๐ ๐๐๐๐๐๐๐๐
Once features are extracted, the model moves to decision-making: ๐ฏ๐ง
๐ ๐ ๐ฅ๐๐ญ๐ญ๐๐ง๐ข๐ง๐ : 2D feature maps are converted into a 1D vector.
๐งฉ ๐ ๐ฎ๐ฅ๐ฅ๐ฒ ๐๐จ๐ง๐ง๐๐๐ญ๐๐ ๐๐๐ฒ๐๐ซ: Combines learned features to perform final high-level reasoning.
๐ ๐๐จ๐๐ญ๐ฆ๐๐ฑ ๐๐๐ฒ๐๐ซ: Converts scores into probabilities for each class (e.g., Cat vs. Dog).
\"CNNs taught machines to see the worldโone filter at a time.\" ๐๐๐ค
#AI #DeepLearning #CNN #NeuralNetworks #ComputerVision #Tech
โค7
All you need to know about a basic neural network! ๐ค
#NeuralNetwork #AI #MachineLearning #Tech #DataScience #DeepLearning
#NeuralNetwork #AI #MachineLearning #Tech #DataScience #DeepLearning
โค5
๐ ๐๐๐ ๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐ โ ๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐ (๐๐๐) ๐
GRUs are a simplified yet powerful variation of the LSTM architecture. ๐ง Introduced to solve the vanishing gradient problem while reducing computational overhead, GRUs merge gates to create a more efficient "memory" system. โก๏ธ They are the go-to choice when you need the performance of an LSTM but have limited compute resources or smaller datasets. ๐๐
๐. ๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐ & ๐๐๐๐๐ ๐๐๐ ๐ง
The GRU streamlines the gating process by combining the cell state and hidden state. ๐
๐๐ฉ๐๐๐ญ๐ ๐๐๐ญ๐: Determines how much of the previous memory to keep and how much new information to add. ๐ฅโ๐ค
๐๐๐ฌ๐๐ญ ๐๐๐ญ๐: Decides how much of the past information to forget before calculating the next state. ๐โณ
๐๐๐ง๐๐ข๐๐๐ญ๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง: A "hidden" layer that suggests a potential update based on the current input and the reset memory. ๐งฉ๐
๐. ๐๐๐ ๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐ ๐
Why choose GRU over its predecessor, the LSTM? ๐ค
๐ ๐๐ฐ๐๐ซ ๐๐๐ญ๐๐ฌ: 2 instead of 3, GRUs train faster and use less memory. ๐๐จ
๐๐๐ฌ๐ฌ ๐๐๐ซ๐๐ฆ๐๐ญ๐๐ซ๐ฌ: By merging the cell and hidden states, information flow is more direct. ๐๐
๐๐๐ญ๐ญ๐๐ซ ๐๐ง ๐๐ฆ๐๐ฅ๐ฅ ๐๐๐ญ๐๐ฌ๐๐ญ๐ฌ: GRUs often outperform LSTMs due to having fewer parameters (reducing the risk of overfitting). ๐ฏ๐
๐. ๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐ ๐
๐๐๐: The basic loop; prone to short-term memory loss. ๐โ
๐๐๐๐: The "Heavyweight"; highly accurate but computationally expensive. ๐๏ธโโ๏ธ๐
๐๐๐: The "Lightweight"; optimized for speed and modern efficiency. ๐ชถโก๏ธ
๐. ๐๐๐๐-๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐ ๐
GRUs excel in environments where latency matters: โฑ๏ธ
๐๐จ๐ข๐๐ ๐๐จ ๐๐๐ฑ๐ญ: Converting voice to text with minimal delay. ๐๐
๐๐จ๐ & ๐๐๐ ๐ ๐๐๐ฏ๐ข๐๐๐ฌ: Running sequential models on low-power hardware (like smart sensors). ๐ก๐
๐๐ฎ๐ฌ๐ข๐ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐จ๐ง: Learning the structure of melodies and rhythm for AI-composed audio. ๐ต๐น
๐. ๐๐๐ ๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐๐ ๐งฎ
๐๐ฉ๐๐๐ญ๐ ๐๐๐ญ๐: Unlike LSTMs, which use separate input and forget gates, GRU update handles both simultaneously. ๐๐
๐๐๐ฌ๐๐ญ ๐๐๐ญ๐: Both gates use sigmoid activations to regulate the information flow between 0 and 1. ๐๐
๐๐๐ง๐๐ข๐๐๐ญ๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง: Used to calculate the candidate hidden state before it is merged into the final output. ๐งฉโ๐
๐. ๐๐๐ ๐๐๐๐๐๐๐๐๐๐ ๐
๐๐๐ฌ๐๐ญ: Decide how much of the past to ignore. ๐
๐๐๐ง๐๐ข๐๐๐ญ๐: Create a potential new memory step. ๐
๐๐ฉ๐๐๐ญ๐: Blend the old state and the new candidate based on the update gate's weight. โ๏ธ
๐๐ฎ๐ญ๐ฉ๐ฎ๐ญ: Pass the new hidden state to the next time step. ๐ช๐โโ๏ธ
"GRUs taught machines that sometimes, simplicity is the ultimate sophistication in intelligence." ๐คโจ
#GRU #AI #MachineLearning #DeepLearning #NeuralNetworks #Tech
GRUs are a simplified yet powerful variation of the LSTM architecture. ๐ง Introduced to solve the vanishing gradient problem while reducing computational overhead, GRUs merge gates to create a more efficient "memory" system. โก๏ธ They are the go-to choice when you need the performance of an LSTM but have limited compute resources or smaller datasets. ๐๐
๐. ๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐ & ๐๐๐๐๐ ๐๐๐ ๐ง
The GRU streamlines the gating process by combining the cell state and hidden state. ๐
๐๐ฉ๐๐๐ญ๐ ๐๐๐ญ๐: Determines how much of the previous memory to keep and how much new information to add. ๐ฅโ๐ค
๐๐๐ฌ๐๐ญ ๐๐๐ญ๐: Decides how much of the past information to forget before calculating the next state. ๐โณ
๐๐๐ง๐๐ข๐๐๐ญ๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง: A "hidden" layer that suggests a potential update based on the current input and the reset memory. ๐งฉ๐
๐. ๐๐๐ ๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐ ๐
Why choose GRU over its predecessor, the LSTM? ๐ค
๐ ๐๐ฐ๐๐ซ ๐๐๐ญ๐๐ฌ: 2 instead of 3, GRUs train faster and use less memory. ๐๐จ
๐๐๐ฌ๐ฌ ๐๐๐ซ๐๐ฆ๐๐ญ๐๐ซ๐ฌ: By merging the cell and hidden states, information flow is more direct. ๐๐
๐๐๐ญ๐ญ๐๐ซ ๐๐ง ๐๐ฆ๐๐ฅ๐ฅ ๐๐๐ญ๐๐ฌ๐๐ญ๐ฌ: GRUs often outperform LSTMs due to having fewer parameters (reducing the risk of overfitting). ๐ฏ๐
๐. ๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐ ๐
๐๐๐: The basic loop; prone to short-term memory loss. ๐โ
๐๐๐๐: The "Heavyweight"; highly accurate but computationally expensive. ๐๏ธโโ๏ธ๐
๐๐๐: The "Lightweight"; optimized for speed and modern efficiency. ๐ชถโก๏ธ
๐. ๐๐๐๐-๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐๐ ๐
GRUs excel in environments where latency matters: โฑ๏ธ
๐๐จ๐ข๐๐ ๐๐จ ๐๐๐ฑ๐ญ: Converting voice to text with minimal delay. ๐๐
๐๐จ๐ & ๐๐๐ ๐ ๐๐๐ฏ๐ข๐๐๐ฌ: Running sequential models on low-power hardware (like smart sensors). ๐ก๐
๐๐ฎ๐ฌ๐ข๐ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐จ๐ง: Learning the structure of melodies and rhythm for AI-composed audio. ๐ต๐น
๐. ๐๐๐ ๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐๐ ๐งฎ
๐๐ฉ๐๐๐ญ๐ ๐๐๐ญ๐: Unlike LSTMs, which use separate input and forget gates, GRU update handles both simultaneously. ๐๐
๐๐๐ฌ๐๐ญ ๐๐๐ญ๐: Both gates use sigmoid activations to regulate the information flow between 0 and 1. ๐๐
๐๐๐ง๐๐ข๐๐๐ญ๐ ๐๐๐ญ๐ข๐ฏ๐๐ญ๐ข๐จ๐ง: Used to calculate the candidate hidden state before it is merged into the final output. ๐งฉโ๐
๐. ๐๐๐ ๐๐๐๐๐๐๐๐๐๐ ๐
๐๐๐ฌ๐๐ญ: Decide how much of the past to ignore. ๐
๐๐๐ง๐๐ข๐๐๐ญ๐: Create a potential new memory step. ๐
๐๐ฉ๐๐๐ญ๐: Blend the old state and the new candidate based on the update gate's weight. โ๏ธ
๐๐ฎ๐ญ๐ฉ๐ฎ๐ญ: Pass the new hidden state to the next time step. ๐ช๐โโ๏ธ
"GRUs taught machines that sometimes, simplicity is the ultimate sophistication in intelligence." ๐คโจ
#GRU #AI #MachineLearning #DeepLearning #NeuralNetworks #Tech
โค2
Overfitting ๐๐
๐ค๐ง
#MachineLearning #AI #DataScience #DeepLearning #Algorithm #NeuralNetworks
๐ค๐ง
#MachineLearning #AI #DataScience #DeepLearning #Algorithm #NeuralNetworks
โค4๐2
"Dive into Deep Learning" ๐๐ค is an open-source book that forms the mathematical foundation for large language models. ๐ง ๐
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ๐งฎ๐๐
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ๐๐๐ง
It contains over 1,000 pages ๐ and provides clear explanations, practical examples, and exercises. โ ๐ Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ๐๐๐ค
arxiv.org/pdf/2106.11342 ๐
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ๐งฎ๐๐
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ๐๐๐ง
It contains over 1,000 pages ๐ and provides clear explanations, practical examples, and exercises. โ ๐ Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ๐๐๐ค
arxiv.org/pdf/2106.11342 ๐
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
โค4
FREE MIT books on AI and Machine Learning: ๐๐ค
1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems โฏ Vol 1: mlsysbook.ai/vol1/assets/do โฏ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
โฏ Part 1 : probml.github.io/pml-book/book1
โฏ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems โฏ Vol 1: mlsysbook.ai/vol1/assets/do โฏ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
โฏ Part 1 : probml.github.io/pml-book/book1
โฏ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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โค6
Optimizing the model's performance through Prompt Tuning with the PEFT library.
โจ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge.
๐ฆ First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).
โ The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.
๐ The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.
๐ The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).
๐ Expected output: PEFT Setup: OK
๐ก Prompt Tuning โ an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference.
#PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience
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โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
โจ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge.
๐ฆ First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).
pip install torch transformers peft
โ The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.
from peft import PromptTuningConfig, PromptTuningInit, get_peft_model
from transformers import AutoModelForCausalLM
peft_config = PromptTuningConfig(
task_type="CAUSAL_LM",
prompt_tuning_init=PromptTuningInit.TEXT,
num_virtual_tokens=20,
prompt_tuning_init_text="Classify the sentiment of this text:",
tokenizer_name_or_path="gpt2"
)
๐ The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.
base_model = AutoModelForCausalLM.from_pretrained("gpt2")
peft_model = get_peft_model(base_model, peft_config)
peft_model.print_trainable_parameters()๐ The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).
python3 -c "from peft import PromptTuningConfig; print('PEFT Setup: OK')"๐ Expected output: PEFT Setup: OK
pip uninstall peft -y
๐ก Prompt Tuning โ an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference.
#PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience
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AI PYTHON ๐
Youโve been invited to add the folder โAI PYTHON ๐โ, which includes 14 chats.
โค4๐ฅ1
If you want to finally understand how neural networks actually learn, I recommend these notes from Stanford CS224N. ๐ง
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. ๐
Inside:
โข Chain Rule
โข Computational Graphs
โข Vectorized derivatives
โข Efficient gradient calculation
โข Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). ๐ฅ
These notes just fill this gap. ๐ ๏ธ
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. ๐
Inside:
โข Chain Rule
โข Computational Graphs
โข Vectorized derivatives
โข Efficient gradient calculation
โข Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). ๐ฅ
These notes just fill this gap. ๐ ๏ธ
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
โค2
Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch ๐ง โจ
The Transformerโs attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. ๐
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called โParallaxโ that scales to LLM pretraining and codesigns with Muon. ๐
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. ๐ปโก
More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/
#Parallax #LLM #AI #DeepLearning #Transformer #TechNews
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โ 13 courses live + 40+ coming soon
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The Transformerโs attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. ๐
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called โParallaxโ that scales to LLM pretraining and codesigns with Muon. ๐
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. ๐ปโก
More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/
#Parallax #LLM #AI #DeepLearning #Transformer #TechNews
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โค5
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. ๐
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. ๐ค
Instead of endless Google searches, everything is organized into categories:
โข fundamentals of machine learning
โข neural networks and modern architectures
โข tasks and application areas
โข datasets
โข libraries and tools
โข fairness and AI ethics
โข production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. ๐
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. โ ๏ธ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. ๐ค
Instead of endless Google searches, everything is organized into categories:
โข fundamentals of machine learning
โข neural networks and modern architectures
โข tasks and application areas
โข datasets
โข libraries and tools
โข fairness and AI ethics
โข production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. ๐
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. โ ๏ธ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
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โค2
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Someone spent several months manually writing a 200-page guide on mathematics and the basics of machine learning. ๐
No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. ๐ฏ
Inside:
โข neural networks: backpropagation, SGD, Adam, BatchNorm; โ๏ธ
โข classic ML: SVM, Gradient Boosting, K-Means, PCA; ๐
โข hardware for AI: Tensor Cores, Systolic Arrays, CUDA; ๐ฅ๏ธ
โข transformers: Multi-Head Attention, KV Cache, LoRA; ๐ง
โข computer vision: ViT, CNN, MAE, IoU, NMS, VLM; ๐๏ธ
โข agent systems: ReAct, memory, orchestration, OpenClaw. ๐ค
The author describes it as the material he would have wanted to receive himself several years ago. ๐ฐ๏ธ
And yes, the entire guide is distributed free of charge. ๐
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. ๐ฏ
Inside:
โข neural networks: backpropagation, SGD, Adam, BatchNorm; โ๏ธ
โข classic ML: SVM, Gradient Boosting, K-Means, PCA; ๐
โข hardware for AI: Tensor Cores, Systolic Arrays, CUDA; ๐ฅ๏ธ
โข transformers: Multi-Head Attention, KV Cache, LoRA; ๐ง
โข computer vision: ViT, CNN, MAE, IoU, NMS, VLM; ๐๏ธ
โข agent systems: ReAct, memory, orchestration, OpenClaw. ๐ค
The author describes it as the material he would have wanted to receive himself several years ago. ๐ฐ๏ธ
And yes, the entire guide is distributed free of charge. ๐
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
โจ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
โค3