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sbbq

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sebastianraschka.com 1y ago

PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUs

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sebastianraschka.com 1y ago

Intermediate ML and AI questions and answers for interview prep

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github.com 1y ago

Qwen3 Implemented from Scratch

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sebastianraschka.com 1y ago

Understanding and Coding the KV Cache in LLMs from Scratch

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sebastianraschka.com 1y ago

Coding LLMs from the Ground Up: A Complete Course

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magazine.sebastianraschka.com 1y ago

The State of Reasoning Models

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sebastianraschka.com 1y ago

Understanding Reasoning LLMs

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sebastianraschka.com 1y ago

Implementing a Byte Pair Encoding (BPE) Tokenizer from Scratch

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magazine.sebastianraschka.com 1y ago

AI Research Recap 2024: From New Scaling Laws to Scaling Inference Compute

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magazine.sebastianraschka.com 1y ago

Noteworthy AI Research Papers of 2024 (Part One)

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sebastianraschka.com 1y ago

Collection of 1k LLM Research Papers of 2024

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sebastianraschka.com 1y ago

Understanding Multimodal LLMs: The Main Techniques and Latest Models

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github.com 1y ago

Implementing the Llama 3.2 1B and 3B Architectures from Scratch

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github.com 1y ago

Converting GPT to Llama step-by-step code guide

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magazine.sebastianraschka.com 1y ago

New LLM Pre-Training and Post-Training Paradigms: How Modern LLMs Are Trained

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github.com 2y ago

LLM instruction finetuning from-scratch tutorial

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magazine.sebastianraschka.com 2y ago

Tips for LLM Pretraining and Evaluating Reward Models

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sebastianraschka.com 4y ago

Sharing Deep Learning Research Models: Building a Super Resolution App

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sebastianraschka.com 4y ago

Taking Datasets, DataLoaders, and PyTorch’s New DataPipes for a Spin

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sebastianraschka.com 4y ago

Running PyTorch on the M1 GPU

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sebastianraschka.com 4y ago

Creating Confidence Intervals for Machine Learning Classifiers

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github.com 4y ago

Cyclemoid – a new activation function inspired by cyclical learning rates

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sebastianraschka.com 4y ago

Machine Learning with PyTorch and Scikit-Learn

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github.com 9y ago

3 different ways to do backpropagation in TensorFlow

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Apple M5 chip 9 months ago

The chips are great. Now they just need to improve the quite stagnant laptop hardware to go with it.

I got my first switch in 2017 and still use it as my main console. I used to be a hardcore gamer but as I got older approaching my 40s I appreciate its simplicity and catalog. I game occasionally, maybe 2 hours a week, and it allows me to use it on the couch, bed, favorite chair, whereever I feel most comfortable and relaxed after an intense day of work. That being said, i was excited about a new Switch and must say that I was a bit disappointed because Nintendo always came out with a big surprise regarding their new console designs. On the other hand, I am also just happy that it still retains the handheld form-factor and focus because that's exactly what I love about the original Switch.

Everyone is buying up H100's (and A100's if they can't find H100's). Sure, in 18 month people may lose interest in buying H100's but then there will already be the next Nvidia model everyone wants to buy to get ahead of the competition in terms of compute capabilities.

Bluetooth ususally works pretty reliably for me these days. However, if you connect a larger number of bluetooth devices (headphones, mouse, keyboard, trackpad, etc.) it can become a bit flaky.

Since you don't move keyboards like a mouse or headphones, it helps reduce the number of peripheries connected to your computer, which in turn helps with bluetooth connectivity issues if you have a lot of devices connected.

Oh, and it's one fewer thing to charge.

I think Rapids AI's cuML tried to go into this direction (essentially scikit-learn on the GPU): https://docs.rapids.ai/api/cuml/stable/api.html#logistic-reg.... For some reason it never took really off though.

Btw., going on a tangent, you might like Hummingbird (https://github.com/microsoft/hummingbird). It allows you trained scikit-learn tree-based models to PyTorch. I watched the SciPy talk last year, and it's a super smart & elegant idea.

Author here. I agree with what you said. I wrote my first book with Packt back when I was a student and was like: "cool, a book deal!" Of course, I didn't know about the caveats :P. Yeah, there was very low (/no) quality control. In fact, they introduced a lot of typos during the layouting (apparently, they re-typed the equations by hand!). However, despite all of that, the book was quite successful, so for the subsequent editions, they gave my book much more attention. Personally, I also got much more flexibility regarding deadlines, etc.

Long story short, yeah, there are definitely issues with quality control, and it's really up to the author to make sure that the content is correct and sound. For this particular book, I must say that I worked with a great layouter who paid a lot of attention to detail this time. Also, with their new layout, they no longer had to re-type the equations, and the typesetting looks so much better now. I am pretty happy with how it turned out this time :)