SKU: 24909138604

Bundle Set

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Description

Bundle SetThis bundle set is a must have for every woman out there. Easy to apply, it guarantees you long lasting colors that will go with any skin type. No more worrying about reapplication. The formula is made to perfection. Keeping your lips hydrated , giving you a fresh look throughout the day. The Accenti signature series will leave you feeling confident with a sweet evocative aroma. How to use? * For best results, prep your lips by gently exfoliating and

This bundle set is a must have for every woman out there. Easy to apply, it guarantees you long lasting colors that will go with any skin type.  No more worrying about reapplication.  The formula is made to perfection. Keeping your lips hydrated , giving you a fresh look throughout the day.  The Accenti signature series will leave you feeling confident with a sweet evocative aroma. 

How to use?

* For best results, prep your lips by gently exfoliating and removing any excess oils.

* Use your favorite lip liner to contour your lips. A darker shade will give you those plump fuller lips.  ( Optional)

* Apply a thin layer of our liquid matte lipstick to fill in the rest.

* Wait for 15 seconds and feel the color absorb deeply into your lips.

* Color appearance will vary based on skin tone.

* You can mix them up to create a fun new shade or use a tissue to blot away for a more natural look.

About Accenti Signature


This innovative formula is highly pigmented, applies smoothly, dries like an opaque matte lipstick and has a velvety finish. Transfer free, our liquid Matte Lipsticks is Paraben and cruelty free. Easy to apply we guarantee you a long lasting color that goes well with all types of skin tone.  No more worrying about reapplication.  This formula is made to perfection. Keeping your lips hydrated and giving you a fresh look throughout the day.  The Accenti signature series will leave you feeling confident with a sweet evocative aroma.

Signature Look

Create a glam fun look with all three of our creamy nude shades. Use Spell bound to design a lusty blush eye shadow. Combine Charm and Temptation to create a completely new lip color! Our bundle set signature look can be used anytime and anywhere!

Ingredients

Isododecane, Cyclopentasiloxane, Polybutene, Nylon-12, Candelilla Cera, Octyldodecanol, Cera Alba, Stearoxymethicone/Dimethicone Copolymer, Silica, Copernicia Cerifera Cera, Glyceryl Hydrogenated Rosinate, Cyclohexasiloxane, Parfum, Silica Dimethyl Silylate, Phenoxyethanol, Propylene Glycol, Decylene Glycol, Imidazolidinyl Urea, Sodium Saccharin, Dimethicone. +/- CI 77891, CI 77491, CI 77492, CI 77499, CI 15850.

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        SKU: 24909138604

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        4.2 ★★★★★
        Based on 5 reviews
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        Product Reviews
        P
        Verified Purchase
        Par
        San Leandro, US
        ★★★★★ 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.
        WAS THIS REVIEW HELPFUL?YesReportShare
        Reviewed in the United States on December 20, 2024
        R
        Verified Purchase
        Richard Hackathorn
        Battle Creek, US
        ★★★★★ 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.
        WAS THIS REVIEW HELPFUL?YesReportShare
        Reviewed in the United States on February 26, 2022
        A
        Verified Purchase
        Amazon Customer
        Waukegan, US
        ★★★★★ 4
        Just learning it
        Format: Paperback
        Nice learning book just have to finish it
        WAS THIS REVIEW HELPFUL?YesReportShare
        Reviewed in the United States on December 10, 2025
        K
        Verified Purchase
        Kindle Customer
        Draper, US
        ★★★★★ 5
        Very useful book
        Format: Paperback
        I use it for the machine learning class I teach.
        WAS THIS REVIEW HELPFUL?YesReportShare
        Reviewed in the United States on May 3, 2026
        T
        Verified Purchase
        Tommy Jonsson
        Dallas, US
        ★★★★★ 5
        Cover many areas in detail and recommendations for more to read for what's outside
        Format: Paperback
        Good book!
        WAS THIS REVIEW HELPFUL?YesReportShare
        Reviewed in the United States on May 4, 2026

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