#MLOps #MachineLearning #DataScience #AI #ModelMonitoring #MLPipelines #Docker #MLSystemDesign #ExperimentTracking #LLMOps #NeuralNetworks #DeepLearning #AITools #MLProjects #MLOpsRoadmap
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In Python, image processing unlocks powerful capabilities for computer vision, data augmentation, and automation—master these techniques to excel in ML engineering interviews and real-world applications! 🖼
more explain: https://hackmd.io/@husseinsheikho/imageprocessing
#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3
# PIL/Pillow Basics - The essential image library
from PIL import Image
# Open and display image
img = Image.open("input.jpg")
img.show()
# Convert formats
img.save("output.png")
img.convert("L").save("grayscale.jpg") # RGB to grayscale
# Basic transformations
img.rotate(90).save("rotated.jpg")
img.resize((300, 300)).save("resized.jpg")
img.transpose(Image.FLIP_LEFT_RIGHT).save("mirrored.jpg")
more explain: https://hackmd.io/@husseinsheikho/imageprocessing
#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3
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ML Engineer, LLM Engineer, take note: TorchCode
A platform with practice tasks for basic implementations in PyTorch and questions on Transformer, which are often encountered in interviews.
→ Gathers in 39 structured tasks typical for #ML #interviews - implementations of operators, modules, and architectures in #PyTorch.
→ Provides auto-checking, gradient checking, time measurement, and instant feedback, so that the practice more closely resembles #LeetCode for interviews.
→ Built on the basis of Jupyter Notebook, while supporting one-click reset, hints, reference solutions, and progress tracking.
→ Covers such frequent topics as ReLU, Softmax, LayerNorm, Attention, RoPE, Flash Attention, #LoRA, $MoE, and others.
→ Supports online mode via Hugging Face Spaces, opening individual tasks in #Google #Colab, and local launch via #Docker.
👉 https://github.com/duoan/TorchCode
A platform with practice tasks for basic implementations in PyTorch and questions on Transformer, which are often encountered in interviews.
→ Gathers in 39 structured tasks typical for #ML #interviews - implementations of operators, modules, and architectures in #PyTorch.
→ Provides auto-checking, gradient checking, time measurement, and instant feedback, so that the practice more closely resembles #LeetCode for interviews.
→ Built on the basis of Jupyter Notebook, while supporting one-click reset, hints, reference solutions, and progress tracking.
→ Covers such frequent topics as ReLU, Softmax, LayerNorm, Attention, RoPE, Flash Attention, #LoRA, $MoE, and others.
→ Supports online mode via Hugging Face Spaces, opening individual tasks in #Google #Colab, and local launch via #Docker.
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GitHub
GitHub - duoan/TorchCode: 🔥 LeetCode for PyTorch — practice implementing softmax, attention, GPT-2 and more from scratch with instant…
🔥 LeetCode for PyTorch — practice implementing softmax, attention, GPT-2 and more from scratch with instant auto-grading. Jupyter-based, self-hosted or try online. - duoan/TorchCode
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