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Description
LEGO® DUPLO® 10426 Train Bridge and Tracks Expansion SetRaise a toddlers railway play to the next level with the Train Bridge and Tracks Expansion Set (10426). This creative build and rebuild toy lets toddlers add an elevated bridge to their LEGO DUPLO Town interactive train sets (10427 and 10428, sold separately) and elongate the route with 8 straight train track pieces that connect to any LEGO DUPLO train set. It also comes with a buildable bridge and a raisable barrier. Toddlers aged 2+ will have lots
Raise a toddler’s railway play to the next level with the Train Bridge and Tracks Expansion Set (10426). This creative build-and-rebuild toy lets toddlers add an elevated bridge to their LEGO® DUPLO® Town interactive train sets (10427 and 10428, sold separately) and elongate the route with 8 straight train track pieces that connect to any LEGO DUPLO train set. It also comes with a buildable bridge and a raisable barrier. Toddlers aged 2+ will have lots of fun playing the engineer letting through the train while practicing their fine motor skills. This set’s action brick makes the interactive trains from sets 10427 and 10428 stop and play fun sounds as they approach it.
This kids’ learning toy boosts toddlers’ development. Extending the train track to run over the bridge teaches spatial awareness. Combining the toy track to determine the train’s destination promotes prediction and problem-solving skills and helps toddlers develop patience and focus.
- Train track toy set – Toddlers aged 2+ extend their LEGO® DUPLO® railway adventures and learn development skills with the Train Bridge and Tracks Expansion Set
- Comes with lots of extra pieces to grow the fun – This creative building toy includes 8 track pieces, 1 action brick, a buildable bridge, raisable barrier and an engineer figure with a cell phone
- Track building that connects to toy train sets – This set combines with LEGO® DUPLO® Town interactive train sets (sold separately) to expand toddlers’ learning journeys even further
- Anytime gift idea – This extension set makes a fun treat for any toddler who loves to play with their LEGO® DUPLO® interactive toy train set, sold separately
- Skill-building playsets – LEGO® DUPLO® build-and-rebuild interactive train toys are expertly designed to develop toddlers’ creative problem-solving, patience, focus and perseverance skills
- Quality toddler toys – All elements of this LEGO® DUPLO® toy have been tested to the max to ensure they fulfill demanding child safety standards
- Measurements – This 42-piece set includes a raised bridge measuring over 9.5 in. (24 cm) high, 41 in. (105 cm) wide and 6 in. (16 cm) deep
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4.7 ★★★★★
Based on 18 reviews
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Product Reviews
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents.
The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!!
The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!!
I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge.
Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents.
Personally I was disappointed by lack of any example on time series.
Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book.
For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch.
For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min).
From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures.
I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications.
Several textbook editions later, what is different about this new edition?
First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills.
Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like.
Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
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