Neural networks python second par wong mei (8 résultats)

Etat : New.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Etat : new. Paperback. 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.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.Key LearningsBuild modern architecture by hand in NumPy, and then rebuild it in PyTorch.Split work cleanly and write one training loop that drives architecture unchanged.Diagnose overfitting with learning curves, then apply the full regularization toolkit.Design convolutional networks that treat images as spatial objects, not flat vectors.Implement attention and transformers from scaled dot-product to full encoder blocks.Cut images into patches and train vision transformers from scratch.Build diffusion models that generate crisp images through iterative denoising.Compare architectures honestly using one dataset, one seed, one pipeline.Recognize the reusable parts inside any new architecture paper you read.Table of ContentSetting up Neural Network StackData Pipelines with NumPy and PandasFeedforward Networks in DepthConvolutional Networks for Visual TasksAutoencoders and Variational AutoencodersAttention and TransformersVision TransformersKolmogorov-Arnold NetworksState Space ModelsDiffusion Models and Diffusion TransformersMultimodal LLMs This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 170 pp. Englisch.

Paperback. Etat : new. Paperback. 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.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.Key LearningsBuild modern architecture by hand in NumPy, and then rebuild it in PyTorch.Split work cleanly and write one training loop that drives architecture unchanged.Diagnose overfitting with learning curves, then apply the full regularization toolkit.Design convolutional networks that treat images as spatial objects, not flat vectors.Implement attention and transformers from scaled dot-product to full encoder blocks.Cut images into patches and train vision transformers from scratch.Build diffusion models that generate crisp images through iterative denoising.Compare architectures honestly using one dataset, one seed, one pipeline.Recognize the reusable parts inside any new architecture paper you read.Table of ContentSetting up Neural Network StackData Pipelines with NumPy and PandasFeedforward Networks in DepthConvolutional Networks for Visual TasksAutoencoders and Variational AutoencodersAttention and TransformersVision TransformersKolmogorov-Arnold NetworksState Space ModelsDiffusion Models and Diffusion TransformersMultimodal LLMs This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Etat : new. Paperback. 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.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.Key LearningsBuild modern architecture by hand in NumPy, and then rebuild it in PyTorch.Split work cleanly and write one training loop that drives architecture unchanged.Diagnose overfitting with learning curves, then apply the full regularization toolkit.Design convolutional networks that treat images as spatial objects, not flat vectors.Implement attention and transformers from scaled dot-product to full encoder blocks.Cut images into patches and train vision transformers from scratch.Build diffusion models that generate crisp images through iterative denoising.Compare architectures honestly using one dataset, one seed, one pipeline.Recognize the reusable parts inside any new architecture paper you read.Table of ContentSetting up Neural Network StackData Pipelines with NumPy and PandasFeedforward Networks in DepthConvolutional Networks for Visual TasksAutoencoders and Variational AutoencodersAttention and TransformersVision TransformersKolmogorov-Arnold NetworksState Space ModelsDiffusion Models and Diffusion TransformersMultimodal LLMs This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.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.Key LearningsBuild modern architecture by hand in NumPy, and then rebuild it in PyTorch.Split work cleanly and write one training loop that drives architecture unchanged.Diagnose overfitting with learning curves, then apply the full regularization toolkit.Design convolutional networks that treat images as spatial objects, not flat vectors.Implement attention and transformers from scaled dot-product to full encoder blocks.Cut images into patches and train vision transformers from scratch.Build diffusion models that generate crisp images through iterative denoising.Compare architectures honestly using one dataset, one seed, one pipeline.Recognize the reusable parts inside any new architecture paper you read.Table of ContentSetting up Neural Network StackData Pipelines with NumPy and PandasFeedforward Networks in DepthConvolutional Networks for Visual TasksAutoencoders and Variational AutoencodersAttention and TransformersVision TransformersKolmogorov-Arnold NetworksState Space ModelsDiffusion Models and Diffusion TransformersMultimodal LLMs.…

Taschenbuch. Etat : Neu. Neural Networks with Python, Second Edition | Explore Transformers, ViTs, Diffusion, KANs, and SSMs using Python, NumPy and PyTorch | Mei Wong | Taschenbuch | Englisch | 2026 | GitforGits | EAN 9789349174498 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …