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kit de d marrage bosch 1x procore 18 v 4 0 ah professional batterie li ion 1600a016gb chargeur gal 18v 160 professional 1600a02t5gContenu de la livraison: 1x Bosch GBA 18 V 4,0 Ah Batterie insertion ProCORE 1x Bosch GAL 18V 160 Professional Chargeur de batterie Description du produit: La batterie ProCORE Professional de Bosch est une batterie de 18 volts avec 4,0 Ah, base sur la technologie Li Ion. La technologie ProCORE convainc par sa puissance, comparable celle du moteur de 1600 watts d'un outil filaire, et fournit ainsi suffisamment de force pour les applications les plus
Contenu de la livraison:- 1x Bosch GBA 18 V 4,0 Ah Batterie à insertion ProCORE
- 1x Bosch GAL 18V-160 Professional Chargeur de batterie
Description du produit:
La batterie ProCORE Professional de Bosch est une batterie de 18 volts avec 4,0 Ah, basée sur la technologie Li-Ion. La technologie ProCORE convainc par sa puissance, comparable à celle du moteur de 1600 watts d'un outil filaire, et fournit ainsi suffisamment de force pour les applications les plus lourdes avec des outils sans fil. Il fournit ainsi 87% de puissance en plus que les outils sans fil 18 volts normaux et garantit des performances élevées en permanence. Grâce à la technologie cellulaire la plus récente, la batterie ProCORE fournit la même puissance qu'une batterie standard de 4,0 Ah, mais avec des dimensions et un poids nettement plus faibles. Grâce à la technologie COOLPACK, la batterie ProCORE a une durée de vie 135% plus longue qu'une batterie standard. Elle permet également d'éviter l'échauffement de la batterie.
Le GAL 18V-160 Professional de Bosch est un chargeur ultra-rapide qui recharge tes outils électriques sans fil rapidement et efficacement, évitant ainsi les frustrations liées aux chargeurs lents. Avec une vitesse de charge impressionnante, tu peux recharger complètement ta batterie ProCORE18V 8,0 Ah Professional en seulement 44 minutes. Le chargeur propose trois modes de charge pratiques : Standard, PowerBoost et Long Life, qui te permettent d'adapter la puissance de charge en fonction de tes besoins. Le mode PowerBoost est idéal lorsque tu as besoin d'une charge complète rapide, tandis que le mode Long Life permet une charge plus douce jusqu'à 80% afin de prolonger la durée de vie de ta batterie. Une particularité du GAL 18V-160 est la technologie Active Air Cooling, qui fonctionne avec deux ventilateurs intégrés pour maintenir les batteries au frais pendant la charge. Ceci est particulièrement utile lorsque les batteries sont devenues chaudes suite à une utilisation prolongée. Le chargeur est non seulement compatible avec le système 18 V Bosch Professional, mais aussi avec l'alliance de batteries AMPShare, ce qui en fait un choix polyvalent pour différents outils. L'indicateur d'état de charge est visualisé par cinq voyants LED qui indiquent à tout moment l'état exact de la charge, afin que tu saches toujours quand tes batteries sont prêtes à l'emploi. Avec le GAL 18V-160 Professional, ton flux de travail n'est pas perturbé et ta productivité reste élevée.
Caractéristiques techniques:
Fabricant : Bosch
Nom du fabricant : Starter Set
ProCORE 18 V 4,0 Ah Professional :
Tension de la batterie : 18 V
Capacité de la batterie : 4,0 Ah / 4000 mAh
Poids : 515 g
Dimensions (LxlxH) : 120x76x51 mm
GAL 18V-160 Professional :
Numéro de fabricant : 1600A02T5G
Tension de charge de la batterie : 14,4 - 18V
Dimensions de l'emballage (largeur x longueur x hauteur) : 140 x 250 x 95 mm
Courant de charge : 16 ampères
Poids : 950 g
En cas d'utilisation commerciale, veuillez respecter les prescriptions de construction !
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4.6 ★★★★★
Based on 22 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