|
× |
 |
The Elements of Statistical Learning |
709 EGP |
|
1418 EGP |
|
× |
 |
Patterns of Enterprise Application Architecture |
329 EGP |
|
329 EGP |
|
× |
 |
Photonics |
799 EGP |
|
799 EGP |
|
× |
 |
The Data Science Handbook |
499 EGP |
|
499 EGP |
|
× |
 |
Cryptocurrency All-in-One For Dummies |
739 EGP |
|
739 EGP |
|
× |
 |
3D Math Primer for Graphics and Game Development |
759 EGP |
|
759 EGP |
|
× |
 |
Rails AntiPatterns |
449 EGP |
|
449 EGP |
|
× |
 |
Mastering Machine Learning Algorithms |
729 EGP |
|
729 EGP |
|
× |
 |
Inside the Android OS |
399 EGP |
|
399 EGP |
|
× |
 |
Architecting Vue.js 3 Enterprise-Ready Web Applications |
399 EGP |
|
399 EGP |
|
× |
 |
The Manga Guide to Linear Algebra |
399 EGP |
|
399 EGP |
|
× |
 |
Learning SQL |
219 EGP |
|
219 EGP |
|
× |
 |
JavaScript & jQuery, The Missing Manual |
659 EGP |
|
659 EGP |
|
× |
 |
How Linux Works |
519 EGP |
|
519 EGP |
|
× |
 |
Data Science on the Google Cloud Platform |
489 EGP |
|
489 EGP |
|
× |
 |
Building Vue.js Applications with GraphQL |
419 EGP |
|
419 EGP |
|
× |
 |
Design Patterns for Embedded Systems in C |
529 EGP |
|
529 EGP |
|
× |
 |
Deep Learning, MIT |
419 EGP |
|
419 EGP |
|
× |
 |
Black Hat Go |
459 EGP |
|
459 EGP |
|
× |
 |
Deep Learning for Dummies |
459 EGP |
|
459 EGP |
|
× |
 |
Introducing Charticulator for Power BI |
489 EGP |
|
489 EGP |
|
× |
 |
Mastering Embedded Linux Programming |
709 EGP |
|
709 EGP |
|
× |
 |
Deep Learning Illustrated |
489 EGP |
|
489 EGP |
|
× |
 |
Artificial Intelligence in Finance |
529 EGP |
|
529 EGP |
|
× |
 |
Concurrency in .NET |
589 EGP |
|
589 EGP |
|
× |
 |
M Is for (Data) Monkey |
389 EGP |
|
389 EGP |
|
× |
 |
Pro iOS Testing |
429 EGP |
|
429 EGP |
|
× |
 |
Oracle Siebel CRM 8 Developers Handbook |
589 EGP |
|
1178 EGP |
|
× |
 |
Practical IoT Hacking |
519 EGP |
|
519 EGP |
|
× |
 |
Practical Automated Machine Learning on Azure |
359 EGP |
|
359 EGP |
|
× |
 |
Patterns, Principles, and Practices of Domain-Driven Design |
729 EGP |
|
729 EGP |
|
× |
 |
Algorithms Illuminated 1 |
369 EGP |
|
369 EGP |
|
× |
 |
Feature Engineering for Machine Learning |
369 EGP |
|
369 EGP |
|
× |
 |
Mathematics for Machine Learning |
229 EGP |
|
229 EGP |
|
× |
 |
Cloud Native Go, Building Web Applications and Microservices for the Cloud with Go and React |
389 EGP |
|
389 EGP |
|
× |
 |
Problem Solving with C++ |
969 EGP |
|
969 EGP |
|
× |
 |
Hands-On Machine Learning with ML.NET |
409 EGP |
|
409 EGP |
|
× |
 |
Deep Learning in Natural Language Processing |
449 EGP |
|
449 EGP |
|
× |
 |
Java Generics and Collections |
409 EGP |
|
409 EGP |
|
× |
 |
Deep Learning for Natural Language Processing, Creating Neural Networks with Python |
419 EGP |
|
419 EGP |
|
× |
 |
Get Programming with Go |
459 EGP |
|
459 EGP |
|
× |
 |
Next Generation AI Language Models in Research |
449 EGP |
|
449 EGP |
|
× |
 |
Kotlin Cookbook |
389 EGP |
|
389 EGP |
|
× |
 |
Software Engineering, A Methodical Approach |
599 EGP |
|
599 EGP |
|
× |
 |
High Performance Spark |
459 EGP |
|
459 EGP |
|
× |
 |
Probabilistic Machine Learning for Finance and Investing |
399 EGP |
|
399 EGP |
|
× |
 |
Algorithms Illuminated 4 |
399 EGP |
|
399 EGP |
|
× |
 |
Information Theory, Inference and Learning Algorithms |
629 EGP |
|
629 EGP |
|
|
