DOWNLOAD THE APP
Customer Services
Copyright © 2025 Desertcart Holdings Limited
DOWNLOAD THE APP
desertcart.co.jp: Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 : Raschka, Sebastian, Mirjalili, Vahid: Foreign Language Books Review: C’est une livre utile ! Review: I have not finished this book and I just reached chapter 16, but here are my key takeaways for this book: 1. Everything before chapter 13, before the book fully gets into deep learning and TensorFlow, are great. With already some background in python for data analysis (I have also taken the Andrew Ng's Coursera course on Machine Learning), this book supplements my knowledge greatly. The biggest highlight I would say is that it introduces you JUST ENOUGH concepts for you to understand how everything works. In addition, the contents are structured really well, too. If I were to rate this section of the book, I would give 10/10 although it would be better to have some exercises, you can always practice using Kaggle datasets. 2. Since chapter 13 when the book gets into deep learning, things get worse a little bit... The contents are still good in general, however the connections between contents might not be the case. The connections between contents are important for new learners because that helps them to understand how A leads to B and then leads to C. Here, I found the actual TensorFlow documentation a really good material to review along with the book. After reviewing those documentations, coming back to this book allows me to comprehend much more than reading the first time. In addition, if you are not careful enough, the deep learning sections also seems to have accuracy issues with its contents that could confuse people. Even though I have not finished the book, I would give 9/10 for everything I have read for deep learning.
| Amazon Bestseller | #110,975 in Foreign Language Books ( See Top 100 in Foreign Language Books ) #131 in Natural Language Processing Software #180 in Python Programming #214 in Computer Neural Networks |
| Customer Reviews | 4.5 4.5 out of 5 stars (463) |
| Dimensions | 7.5 x 1.74 x 9.25 inches |
| Edition | 3rd ed. |
| ISBN-10 | 1789955750 |
| ISBN-13 | 978-1789955750 |
| Item Weight | 1.3 Kilograms |
| Language | English |
| Print length | 772 pages |
| Publication date | December 12, 2019 |
| Publisher | Packt Publishing |
T**9
C’est une livre utile !
T**Y
I have not finished this book and I just reached chapter 16, but here are my key takeaways for this book: 1. Everything before chapter 13, before the book fully gets into deep learning and TensorFlow, are great. With already some background in python for data analysis (I have also taken the Andrew Ng's Coursera course on Machine Learning), this book supplements my knowledge greatly. The biggest highlight I would say is that it introduces you JUST ENOUGH concepts for you to understand how everything works. In addition, the contents are structured really well, too. If I were to rate this section of the book, I would give 10/10 although it would be better to have some exercises, you can always practice using Kaggle datasets. 2. Since chapter 13 when the book gets into deep learning, things get worse a little bit... The contents are still good in general, however the connections between contents might not be the case. The connections between contents are important for new learners because that helps them to understand how A leads to B and then leads to C. Here, I found the actual TensorFlow documentation a really good material to review along with the book. After reviewing those documentations, coming back to this book allows me to comprehend much more than reading the first time. In addition, if you are not careful enough, the deep learning sections also seems to have accuracy issues with its contents that could confuse people. Even though I have not finished the book, I would give 9/10 for everything I have read for deep learning.
N**N
If you're just starting out this book might be slightly too advanced. It's perfect for junior data scientists who want to refresh their knowledge or touch up on some areas they haven't seen before. In a similar way it would be perfect for people with technical backgrounds such as computer science and mathematics who haven't done any machine learning before but want to.
Q**.
Quite good textbook arrived in proper condition.
A**7
Best ML python book
Trustpilot
2 weeks ago
4 days ago