- Paperback: 384 pages
- Publisher: Manning Publications; 1 edition (December 22, 2017)
- Language: English
- ISBN-10: 1617294438
- ISBN-13: 978-1617294433
- Product Dimensions: 7.4 x 0.8 x 9.2 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
- Average Customer Review: 44 customer reviews
- Amazon Best Sellers Rank: #6,052 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Deep Learning with Python 1st Edition
Use the Amazon App to scan ISBNs and compare prices.
Fulfillment by Amazon (FBA) is a service we offer sellers that lets them store their products in Amazon's fulfillment centers, and we directly pack, ship, and provide customer service for these products. Something we hope you'll especially enjoy: FBA items qualify for FREE Shipping and Amazon Prime.
If you're a seller, Fulfillment by Amazon can help you increase your sales. We invite you to learn more about Fulfillment by Amazon .
See the Best Books of 2018 So Far
Looking for something great to read? Browse our editors' picks for the best books of the year so far in fiction, nonfiction, mysteries, children's books, and much more.
Frequently bought together
Customers who bought this item also bought
From the Publisher
Who should read this book
- If you’re a data scientist familiar with machine learning, this book will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning
- If you’re a deep-learning expert looking to get started with the Keras framework, you’ll find this book to be the best Keras crash course available
- If you’re a graduate student studying deep learning in a formal setting, you’ll find this book to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices
About This Book
This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether you’re a practicing machine-learning engineer, a software developer, or a college student, you’ll find value in these pages. This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of machine learning and deep learning. You’ll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems. The code examples use the Python deep-learning framework Keras, with Tensor- Flow as a back-end engine. Keras, one of the most popular and fastest-growing deeplearning frameworks, is widely recommended as the best tool to get started with deep learning.
After reading this book, you’ll have a solid understand of what deep learning is, when it’s applicable, and what its limitations are. You’ll be familiar with the standard workflow for approaching and solving machine-learning problems, and you’ll know how to address commonly encountered issues. You’ll be able to use Keras to tackle real-world problems ranging from computer vision to natural-language processing: image classification, timeseries forecasting, sentiment analysis, image and text generation, and more.
This book is written for people with Python programming experience who want to get started with machine learning and deep learning. But this book can also be valuable to many different types of readers. Even technically minded people who don’t code regularly will find this book useful as an introduction to both basic and advanced deep-learning concepts.
In order to use Keras, you’ll need reasonable Python proficiency. Additionally, familiarity with the Numpy library will be helpful, although it isn’t required. You don’t need previous experience with machine learning or deep learning: this book covers from scratch all the necessary basics. You don’t need an advanced mathematics background, either—high school–level mathematics should suffice in order to follow along.
About the Author
Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He blogs about deep learning at blog.keras.io.
Top customer reviews
There was a problem filtering reviews right now. Please try again later.
Overall this book is more about practical techniques and python code (in Keras) than about deep learning math/theory. This is probably what the majority of readers are looking for. It's a great synthesis of the most important techniques now (start of 2018), which is hard to get just from reading papers.
I would recommend complementing this book with two others:
1) as mentioned above: Deep Learning (Adaptive Computation and Machine Learning series)
2) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
So, why the four stars? Because the book is rather "paint by the numbers". The presentation is filled with "Now you'll do this.." followed by working blocks of code for the student to enter and run. But there are no exercises, code or mathematical. Even the standard backpropagation algorithm is only qualitatively described -- nice pictures of gradient descent in 2 dimensions, but no hard equations. (After all, Keras does it all for you, right?) And as the book ventures into more advanced areas like GANs, VAEs, etc the presentation is increasingly high-level and nonmathematical, providing only a feel for the topics without deep comprehension. Given the depth of the math involved, I suppose I can't blame Chollet for a bit of handwaving. But more rigor with deeper explanations would have been nice.
The first 100 pages or so is on general deep learning and it is extremely basic. Even though I new these concepts I still found it difficult to follow. The writing is really bad.
The more advanced concepts are not any better. Here is an example of the last section I read on page 172 before putting the book down for good.
From the section Visualizing heartmaps of class activation
"This general category of techniques is called class activation map ( CAM ) visualization,
and it consists of producing heatmaps of class activation over input images. A class acti-
vation heatmap is a 2D grid of scores associated with a specific output class, computed
for every location in any input image, indicating how important each location is with
respect to the class under consideration. For instance, given an image fed into a dogs-
versus-cats convnet, CAM visualization allows you to generate a heatmap for the class
“cat,” indicating how cat-like different parts of the image are, and also a heatmap for the
class “dog,” indicating how dog-like parts of the image are.
The specific implementation you’ll use is the one described in “Grad- CAM : Visual
Explanations from Deep Networks via Gradient-based Localization.” 2 It’s very simple:
it consists of taking the output feature map of a convolution layer, given an input
image, and weighing every channel in that feature map by the gradient of the class
with respect to the channel. Intuitively, one way to understand this trick is that you’re
weighting a spatial map of “how intensely the input image activates different chan-
nels” by “how important each channel is with regard to the class,” resulting in a spatial
map of “how intensely the input image activates the class.”
Consider the image of two African elephants shown in figure 5.34 (under a Creative
Commons license), possibly a mother and her calf, strolling on the savanna. Let’s con-
vert this image into something the VGG16 model can read: the model was trained on
images of size 224 × 244, preprocessed according to a few rules that are packaged in
the utility function keras.applications.vgg16.preprocess_input . So you need to
load the image, resize it to 224 × 224, convert it to a Numpy float32 tensor, and apply
these preprocessing rules.
After reading this several times I still did not have a good mental formulation of how these heatmaps are put together. I also did not see the need to put in the paragraph about vgg16 preprocessing right here. It is just a distraction from the matter at hand. The whole book is like this. I was wasting too much energy trying to understand what I believe are basic concepts.
I recommend Ina Goodfellow's book on Deep Learning as well as Aurelien Geron's book. For Keras I would recommend the Deeplearning.AI course on Coursera as well as Deep Learning A-Z on Udemy. I do not know of a good Keras book at this time.