SKU: 91621930665

Insulated Lunch Box Cooler Bag - Thermal Food Storage for Work & School

$26.95 $29.95
Save 10%
No.2 Bestseller on prudentsource.com >

Pay in installments of $7.49 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Jul 19 - Jul 24

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

Insulated Lunch Box Cooler Bag - Thermal Food Storage for Work & SchoolSoggy sandwiches, warm yoghurt, and melted snacks by lunchtimesound familiar? It's frustrating when your carefully packed lunch turns unappetising before you even get to enjoy it. This insulated cooler bag keeps your food fresh and at the right temperature for hours, so you can actually look forward to lunch instead of dreading it. Why Customers Love It Stays Fresh Longer Aluminium film lining locks in cold for hours, meaning your salads stay crisp

Soggy sandwiches, warm yoghurt, and melted snacks by lunchtime—sound familiar? It's frustrating when your carefully packed lunch turns unappetising before you even get to enjoy it. This insulated cooler bag keeps your food fresh and at the right temperature for hours, so you can actually look forward to lunch instead of dreading it.

Why Customers Love It

❄️
Stays Fresh Longer
Aluminium film lining locks in cold for hours, meaning your salads stay crisp and drinks stay chilled until you're ready to eat.
🎒
Perfectly Portable
Compact 21cm design fits easily in your work bag or backpack, so you can carry nutritious meals without the bulk.
💧
Spill-Safe Design
Water-resistant Oxford cloth exterior protects against leaks and splashes, which means no more ruined bags or embarrassing messes.
🌟
Lightweight Convenience
At just 100g, it adds virtually no weight to your daily carry, making healthy eating effortless wherever you go.

Keep Your Lunch as Fresh as When You Packed It

The dual-layer insulation system combines durable Oxford cloth with a reflective aluminium film lining that actively maintains temperature. Whether you're packing cold salads for a hot Australian summer day or keeping soup warm during winter, your food stays at the ideal temperature for 4-6 hours. No more settling for expensive takeaway because your packed lunch isn't appetising anymore.

Fits Your Life, Not the Other Way Around

The thoughtfully sized 21×17×15cm interior holds multiple containers, snacks, and a drink bottle without feeling cramped or oversized. The sturdy carry handle makes it easy to grab and go, while the compact footprint means it won't dominate your desk or locker. Perfect for office workers, students, tradies on site, or families heading to the park for a picnic.

The Insulated Lunch Box Difference

Feature Insulated Lunch Box Cooler Bag Others
Thermal insulation lining ✓ ✗
Water-resistant exterior ✓ ✗
Lightweight construction (100g) ✓ ✗
Compact yet spacious design ✓ ✓
Easy-carry handle ✓ ✓
Durable Oxford cloth material ✓ ✗

Product Details

Specifications

  • Material: Oxford cloth with aluminium film lining
  • Dimensions: 21cm × 17cm × 15cm
  • Weight: 100g
  • Closure: Zip top

What's in the Box

  • 1× Insulated lunch cooler bag

Your Questions Answered

Providing clarity and confidence for your purchase.

🛡️

Try Insulated Lunch Box Cooler Risk-Free!

Backed by our 30-day money-back guarantee. If you're not completely satisfied, we'll make it right — no questions asked.

✅ 30-Day Returns✅ Secure Checkout✅ Fast Dispatch

Customers Adore the Look and Feel!

⭐⭐⭐⭐⭐

⭐⭐⭐⭐⭐

"Perfect size for my work lunches! Keeps my salad cold even on 35-degree days, and it's so light I barely notice it in my bag. Wish I'd bought one sooner."

Emma P. · Sydney, NSW
⭐⭐⭐⭐⭐

"Great little lunch bag. Fits my containers and a drink bottle with room to spare. The insulation actually works—my yoghurt was still cold at 1pm. Very happy with this purchase."

David L. · Melbourne, VIC
⭐⭐⭐⭐

"Does the job well for the price. My daughter uses it for school and it keeps her lunch fresh. Only minor issue is the zip can be a bit stiff, but overall really pleased."

Rachel H. · Brisbane, QLD
⭐⭐⭐⭐⭐

"Brilliant for work on building sites. Keeps my lunch cool in the ute all morning, and it's tough enough to handle being thrown around. Definitely recommend."

Tom W. · Perth, WA

🚚 Fast Dispatch — Shipped within 2 business days
🛡️ Secure Packaging — Carefully packed to arrive safely
🔄 30-Day Returns — Not happy? We'll sort it out
💬 Friendly Support — Real people, fast responses

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 91621930665

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.6 ★★★★★
Based on 5 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
X
Verified Purchase
0x00000000:00000000
Battle Creek, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
Z
Verified Purchase
Zygerian99
San Leandro, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on January 21, 2020
S
Verified Purchase
Shannon
Bozeman, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
W
Verified Purchase
William P Ross
Belleville, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Boise, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026

recommand products

Sculpt & Paint: Creature Lab Kit
29.99
Bosch Spoke Magnet
19.99
1.00 CT Princess Cut Pavé Band Solitaire Engagement Ring in Lab Grown Diamond - 14K Solid Gold, Bridal Gift for Her, Everyday Fine Jewelry
3761.39
20 x Plantronics Blackwire C3220 Stereo USB-A Headset - Refurbished
250.00
Sparkling Bezel and Cross Chain Bracelet in 14K Solid Gold, 0.13 TCW Round Cut Lab Created Diamonds, Birthday Gift For Her, Faith Jewelry
2337.59