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Description
newborn in britax car seat Britax Cypress Infant Car Seat with Alpine Anti-Rebound BaseCherish the journey together with the Cypress infant car seat and Alpine base. This rear facing car seat offers convenience, premium style, and trusted Britax safety, giving you the assurance you need on your first ride home and every drive after. Our exclusive ClickTight technology provides a simple installation in 3 easy steps: open, thread and buckle, click it closed. This newborn car seat offers the perfect combination of style and convenience.
Cherish the journey together with the Cypress™ infant car seat and Alpine™ base. This rear-facing car seat offers convenience, premium style, and trusted Britax safety, giving you the assurance you need on your first ride home and every drive after. Our exclusive ClickTight® technology provides a simple installation in 3 easy steps: open, thread and buckle, click it closed. This newborn car seat offers the perfect combination of style and convenience. The double-knit mélange accents have a soft sheen and a smooth finish for an elegant look and feel. Made with SafeWash® performance fabrics, the cover is washer and dryer-friendly and naturally flame-retardant with no added FR chemicals. Cypress includes three padded inserts to help create a cozier and more customized fit. Two of the inserts layer behind baby’s head and neck for support. This infant car seat also features an aluminum carry handle that easily adjusts with one hand to keep your other hand free. The Alpine base is equipped with a ReboundReduce™ stability bar to help minimize movement so you can travel with confidence.
Features:
- Fit: Designed for rear-facing use only for 4 to 30 pounds and up to 32 inches
- Install with Confidence, Every Time: Britax is the only brand with ClickTight® technology, so you know it can be installed securely on the first ride and beyond
- Premium Mélange Fabrics: Double-knit mélange accents have a soft sheen and a smooth finish
- Three Inserts for a Custom Fit: Includes three padded inserts to help create a cozier and more customized fit; two inserts layer behind baby's head and neck while the third sits underneath the child for support
- Extra-Large Window: Check on your little one through the extra-large mesh window with a quiet magnetic closure
- Ultra-Portable: Lightweight and easy to carry, this newborn car seat features an aluminum carry handle that adjusts easily with one hand; carry handle provides additional stability when used in the upright position
- Washer and Dryer-Friendly: The SafeWash® car seat cover makes cleanup easy so you can keep moving; no more hand-washing or waiting for fabrics to air-dry!
- No Added FR Chemicals: The car seat cover is made with naturally flame-retardant fabrics with no added FR chemicals
- Plush Comfort: Extra cushioning on the head pillow, harness pads, and infant inserts, keeps your child comfy and cozy
- ReboundReduce™ Stability Bar: Helps minimize movement in the event of a crash
- Customize to Baby’s Size: The RightSize™ system provides adjustment points at the hips, shoulders, and between the legs to help you find the perfect fit
- SpaceSaver™ Design: Slim outside, spacious inside! Frees up backseat space while giving your child plenty of room for the ride
- Easy In, Easy Out: Built-in harness holders and flip-forward belly pad clear the way for breezy boarding and unboarding
- Side Impact Tested: Helps protect your child’s head, neck and torso for peace of mind
- SafeCell® Technology: Acts as a crumple zone, helping to keep crash energy away from your child
- Easy Find & Recline: Level indicators make it easy to find the correct installation angle for your vehicle
- ClickTight® Indicator: Helps provide a visual guide while installing the base *Indicator window may vary by model. Refer to user guide for specific instructions
- Base-Free Stability: Extra belt path on the back of the seat provides security when installing without the base
- Sun Protection: UPF 50+ canopy and flip-out visor offer more sun protection while in the car or out for a stroll
- Compatible with Britax Brook™, Brook+ and Gove™ strollers along with select competitive strollers
Specifications:
- Product Weight (Car Seat Only): 9.7 lbs
- Product Dimensions: 28.1"L x 16.9"W x 24.11"H
- Seat Back Compartment Height: 21"
- Harness Slot Heights: 5.5" / 7.5" / 9.5" / 11.5"
- Buckle Strap Depths: 2.25" / 5.5"
- Child Weight: 4-30 lbs
- Rear-facing Harnessed Height: Less than 32"
- Product Weight (Base Only): 10.2 lbs
- Seat Area Depth/Width: 12" / 9.5"
- Shoulder Width: 10"
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4.4 ★★★★★
Based on 21 reviews
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Product Reviews
★★★★★ 5
Excelllent book
Format: Hardcover
As one of the group of Native Americans who landed on Alcatraz with Richard Oakes, I enjoyed this book. Richard was a fantastic man. A good man.
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Reviewed in the United States on February 14, 2019
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people.
THANK YOU Kent for writing this book.
We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
★★★★★ 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