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Položka č.:
64176255
Practical Deep Learning: A Python-Based Introduction, (Paperback)
82% of respondents would recommend this to a friend
Položka č.:
64176255
CZK 1468
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Practical Deep Learning teaches total beginners how to build datasets and train neural networks for DL projects. This is the book you've been waiting for if you've been curious about machine learning.
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What Stands Out
Hands-On Approach
Offers a practical, hands-on learning experience, enabling readers to apply deep learning concepts using Python, making it ideal for beginners seeking applicable skills.
Comprehensive Coverage
Covers a wide array of topics in deep learning, from basics to advanced techniques, ensuring that readers gain a thorough understanding of the field.
Accessible Format
Written in an easy-to-understand language, this book is designed for learners at all levels, making complex concepts more accessible to a broader audience.
Detaily produktu
- Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you've been curious about artificial intelligence and machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math--the book will cover the rest. After an introduction to Python, you'll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models' performance. You'll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector MachinesHow neural networks work and how they're trainedHow to use convolutional neural networksHow to develop a successful deep learning model from scratch You'll conduct experiments along the way, building to a final case study that incorporates everything you've learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | February, 2021 |
| Pages | 464 |
| Reading level | General |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 1 |
| Publisher | No Starch Press |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Educational level | High School |
| Is collectible | N |
| Illustrator | Kevin Howdeshell |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.00 x 1.30 x 9.20 in (17.8 x 3.3 x 23.4 cm) |
| Assembled product weight | 1.94 lb (880 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Beginners in AI
Perfect for those new to artificial intelligence and deep learning concepts, providing clear explanations and practical examples.
-
Python Programmers
Ideal for existing Python developers wanting to enhance their skills in deep learning applications using familiar programming language.
-
Data Scientists
Beneficial for data scientists looking to incorporate deep learning techniques into their data analysis and machine learning workflows.
-
Advanced Users
Not ideal for experienced deep learning practitioners seeking advanced theories or cutting-edge techniques beyond basic frameworks.
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CZK 1468
Objednejte nyní a položka bude u vás okolo středa, říjen 21
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Služby a výhody
- Teaches beginners to build datasets for neural networks
- Focuses on deep learning subfield of machine learning
- Explains core concepts and foundations
- Inspires exploration of deep learning
- Covers Python, scikit-learn, Keras libraries
- Includes building models from scratch and a final case study
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