SKU: 658985752
act 3 the making of monsters

act 3 the making of monsters MAKING MONSTERS: INSIDE STORIES FROM THE CREATORS OF HOLLYWOOD'S MOST ICONIC CREATURES (HOLLYWOOD MONSTERS)

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act 3 the making of monsters MAKING MONSTERS: INSIDE STORIES FROM THE CREATORS OF HOLLYWOOD'S MOST ICONIC CREATURES (HOLLYWOOD MONSTERS)Making Monsters: Inside Stories from the Creators of Hollywood's Most Iconic Creatures (Hollywood Monsters) Hardcover September 16, 2025 by Howard Berger (Author), Marshall Julius (Author) Explore behind the scenes of the greatest monster movies ever made! What makes a great movie monster? Academy Award winning make up effects artist Howard Berger and acclaimed journalist Marshall Julius have spoken to dozens of film industry legends to find out.

Making Monsters: Inside Stories from the Creators of Hollywood's Most Iconic Creatures (Hollywood Monsters) Hardcover – September 16, 2025 by Howard Berger (Author), Marshall Julius (Author) Explore behind the scenes of the greatest monster movies ever made! What makes a great movie monster? Academy Award-winning make-up effects artist Howard Berger and acclaimed journalist Marshall Julius have spoken to dozens of film industry legends to find out.

ACADEMY MUSEUM EXCLUSIVE EVENT: MEET OSCAR®-WINNING MAKE-UP EFFECTS ARTIST HOWARD BERGER AND ACCLAIMED JOURNALIST MARSHALL JULIUS

ABOUT THE BOOK: A celebration of monsters, monster movies and monster movie makers, Making Monsters delivers an illuminating, entertaining and accessible oral history of the genre, gathering an enviable array of A-list talent from make-up and digital effects legends (Tom Savini, Phil Tippett) to directors (John Carpenter, Ti West), actors (Simon Pegg, Barbara Crampton), composers (Michael Giacchino) and writers (Russell T Davies).

Packed with hundreds of images, from film stills to personal, behind-the-scenes pictures from dozens of interview subjects - many never before published - Making Monsters is a treasure trove of monstrous creations, and the stories behind them, that is sure to make fans jump, scream and howl with delight.

ABOUT THE AUTHORS:
Specializing in character prosthetics, animatronics, creatures, and replica animals, Howard Berger has over 800 feature film and television credits that span four decades. In recognition for his work on THE CHRONICLES OF NARNIA: The Lion, The Witch And The Wardrobe, Berger won the Academy Award for Best Make-up, as well as a British Academy Award for Best Achievement in Make-up. In 2012 he was nominated for an Academy Award for designing and creating the portrait makeup for Sir Anthony Hopkins for the FOX Searchlight feature HITCHCOCK. Berger published his first book in 2022 titled MASTERS OF MAKE-UP EFFECTS for Welbeck Publishing in the UK. His second book, MAKING MONSTERS releases worldwide Sept 2025. Berger recently completed 6-years as a Governor for the Makeup Artist and Hair Stylist Branch for the Academy of Motion Picture Arts and Sciences (AMPAS) and he was Vice President of the Academy as well as the Academy Museum Foundation President. Berger resides in Sherman Oaks, CA with his artist wife, Mirjam

Marshall Julius is a film critic, blogger, broadcaster and author, whose previous books include Vintage Geek (September Publishing) and Action! The Action Movie A-Z (Batsford Film Books). He lives in Kent.

Product details
Publisher ‏ : ‎ Welbeck
Publication date ‏ : ‎ September 16, 2025
Language ‏ : ‎ English
Print length ‏ : ‎ 320 pages
ISBN-10 ‏ : ‎ 1802798455
ISBN-13 ‏ : ‎ 978-1802798456
Item Weight ‏ : ‎ 3.55 pounds
Dimensions ‏ : ‎ 9.21 x 0.94 x 11.18 inches

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SKU: 658985752

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Pawtucket, US
★★★★★ 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
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Verified Purchase
Par
Houston, 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.
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Reviewed in the United States on December 20, 2024
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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.
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Reviewed in the United States on February 26, 2022
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Verified Purchase
Amazon Customer
Lowell, 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

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