Neural Networks with Python, Second Edition by Mei Wong (.ePUB)+
File Size: 11.3 MB
Neural Networks with Python, Second Edition: Explore Transformers, ViTs, Diffusion, KANs, and SSMs using Python, NumPy and PyTorch by Mei Wong
Requirements: .ePUB, .PDF reader, 11.3 MB
Overview: This book is the modern neural networks foundation, and it’s taught the way it should be. The way neural networks function has changed, of course, and this second edition has to change too. It’s all rebuilt around the latest versions of Python 3.14, NumPy 2.0 and PyTorch 2.0, so it’ll be the only framework you’ll need to get up to speed quickly. The likes of TensorFlow, Keras, RNNs, GANs and capsule networks are now a thing of the past. Now, the big players in the AI world are convolutional networks, attention and transformers, vision transformers, Kolmogorov-Arnold networks, state space models, diffusion transformers and multimodal language models. This book is all about building a single application using the same data, training it using a single pipeline. That way, you can compare it directly with other applications and see how it really compares. It’s all done by hand in NumPy, then rebuilt in PyTorch, so nothing stays a black box. The field values something different now. Basically, I decided to rewrite the book without just making minor tweaks to the old version. Instead, I asked myself a more challenging question. If a data scientist was starting out today, with no previous experience, what would they actually need to know? The answer turned out to be smaller and sharper than I expected. You need Python, NumPy, PyTorch, and you need to be willing to get your hands dirty building things before you can trust a framework to do it for you. We’ll stick to five libraries, not because more would be a problem, but because keeping it simple shows how well we can organise things. When you download MNIST with just the standard library, you finally see what a dataset loader was hiding. If you write attention as four lines of NumPy before you ever call a PyTorch module, it’s no longer a magic process but just plain arithmetic. Every architecture in this book gets the same treatment. We build it by hand, we understand it, and only then do we let the framework carry it for us. This book is written for data scientists and AI engineers who want depth without the dependency bloat, keeping its toolkit to five libraries and its focus on understanding. The book makes you capable to read any new architecture paper and recognise the parts, because you’ll have built them yourself.
Genre: Non-Fiction > Tech & Devices
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