Pyspark is an essential skill to become a big data engineer Learn Pyspark using the below YT tutorials at FREE of cost
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Python Tutorial for Beginners: Learn Python Programming Easily
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#Python Tutorial For Beginners – Learn Python Easily http://
bit.ly/42TkYol @Great_Learning via @ipfconline1 #Coding #AI #DataScience #100DaysOfCode #AI #Analytics #DataScientists #Statistics #NeuralNetworks #DeepLearning #SupervisedLearning @SpirosMargaris -
CodeT5+ Achieves State-of-the-Art Code Understanding and Generation
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8/ CodeT5+ – supports a wide range of code understanding and generation tasks and different training methods to improve efficacy and computing efficiency; achieves SoTA on tasks like code completion, math programming, and text-to-code retrieval tasks.
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DoReMi: Domain-Weighted Resampling for Efficient Model Training
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7/ DoReMi – trains a small proxy model over domains to produce domain weights without knowledge of downstream tasks; it resamples a dataset with the domain weights which allows using a 280M proxy model to train an 8B model (30x larger) more efficiently.
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Drag Your GAN: Interactive Point-Based Image Control Method
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1/ Drag Your GAN – an approach for controlling GANs that allows dragging points of the image to precisely reach target points in a user-interactive manner.https://t.co/Nbhtle5VRG
— DAIR.AI (@dair_ai) 21 mai 20231/ Drag Your GAN – an approach for controlling GANs that allows dragging points of the image to precisely reach target points in a user-interactive manner.
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Top ML Papers of the Week: DragGAN, CodeT5+, Med-PaLM 2
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Top ML Papers of the Week (May 15 – 21): – DragGAN
– CodeT5+
– StructGPT
– Med-PaLM 2
– Symbol Tuning
– Evidence of Meaning in LLMs
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ML on Graphs: Solving Problems Traditional Methods Failed
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That's a wrap! I build maps for a living, we have used ML on graphs to solve problems where traditional methods failed. Sharing these resources based on my experience!! Find me → @akshay_pachaar Everyday, I share tutorials around ML! Check one of my older threads
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StellarGraph and PyTorch Geometric: ML Libraries for Graph Processing
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StellarGraph | PyTorch Geometric Two of the best libraries to apply ML on graphs right away. Both have great documentation & example to get started! StellarGraph for TensorFlow folks https://
stellargraph.readthedocs.io/en/stable/ PyTorch Geometric for PyTorch folks https://
pytorch-geometric.readthedocs.io/en/latest/ -
Stanford CS224W: Machine Learning with Graphs Course
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Stanford CS224W: Machine Learning with Graphs Offered by Stanford, a comprehensive course for ML on Graphs. Check this out https://
youtube.com/playlist?list=
PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn
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Graph Neural Networks Foundations: Message Passing and Node Embedding
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Foundations of Graph Neural Networks By @PetarV_93 (Scientist at DeepMind) The perfect starting point! What you'll learn:
– GNNs from first principles
– Message passing on graphs
– Node Embedding
– Geometric Deep Learning Check this