Machine Learning in Production: Developing and Optimizing...

Main
Computers - Artificial Intelligence (AI)
Machine Learning in Production:...

Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

Andrew Kelleher, Adam Kelleher

0 / 0

Πόσο σας άρεσε αυτό το βιβλίο;

Ποια είναι η ποιότητα του ληφθέντος αρχείου;

Κατεβάστε το βιβλίο για να αξιολογήσετε την ποιότητά του

Ποια είναι η ποιότητα των ληφθέντων αρχείων;

Κατηγορίες:

Computers - Artificial Intelligence (AI)

Έτος:

2019

Εκδότης:

Addison-Wesley

Γλώσσα:

english

Σελίδες:

274

ISBN 10:

0134116550

ISBN 13:

9780134116556

Αρχείο:

PDF, 16.55 MB

IPFS:

,

english, 2019

Διαβάστε online

Η μετατροπή σε βρίσκεται σε εξέλιξη

Η μετατροπή σε απέτυχε

Ίσως σας ενδιαφέρει

Shlomo Kashani — Deep Learning Interviews

Andrew Kelleher & Adam Kelleher [Andrew Kelleher] — Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications, First Edition

Joanne Rodrigues-Craig [Joanne Rodrigues-Craig] — Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights

Mark Fenner — Machine Learning with Python for Everyone, First Edition

Brian Caffo — Statistical Inference for Data Science - A Companion to the Coursera Statistical Inference Course

Michael Freeman, Joel Ross — Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R, First Edition

Wilkins, Neil — Artificial Intelligence: The Ultimate Guide to AI, The Internet of Things, Machine Learning, Deep Learning + a Comprehensive Guide to Robotics

Hanson, Bill — Machine Learning- The Mastery Bible: The definitive guide to Machine Learning, Data Science, Artificial Intelligence, Neural Networks, and Data Analytics.

Michael Freeman, Joel Ross — Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R

Laura Graesser; Wah Loon Keng — Foundations of Deep Reinforcement Learning: Theory and Practice in Python

Omar Santos, Samer Salam, Hazim Dahir — The AI Revolution in Networking, Cybersecurity, and Emerging Technologies

Walter R. Paczkowski — Deep Data Analytics for New Product Development

Scott Burk, Gary D. Miner — It's All Analytics!: The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government

Carl Shan, Henry Wang, William Chen; Max Song — The Data Science Handbook

Genrich Altshuller — And Suddenly the Inventor Appeared: TRIZ, the Theory of Inventive Problem Solving

Athanasopoulos, George; Hyndman, Rob J. — Forecasting: Principles and Practice

Sean Martin, Ben Szekely, Dean Allemang — The Rise of the Knowledge Graph

Noah Gift, Alfredo Deza — Practical MLOps: Operationalizing Machine Learning Models

Patrick Hall, Navdeep Gill — An Introduction to Machine Learning Interpretability, Second Edition

S. Y. Kung — Kernel Methods and Machine Learning

Dattaraj Rao — Keras to Kubernetes: The Journey of a Machine Learning Model to Production

Sachi Nandan Mohanty (editor), Jyotir Moy Chatterjee (editor), Sarika Jain (editor), Ahmed A. Elngar (editor), Priya Gupta (editor) — Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries

Sachi Nandan Mohanty, G. Nalinipriya, Om Prakash Jena, Achyuth Sarkar — Machine Learning for Healthcare Applications

Doron Mayer — Workflow: a practical guide to the creative process

Paresh Chandra Deka — A Primer on Machine Learning Applications in Civil Engineering

Botev, Zdravko I.; Kroese, Dirk P.; Taimre, Thomas; Vaisman, Radislav — Data science and machine learning. Mathematical and statistical methods

Nyhoff, Jeffrey; Nyhoff, Larry R — Processing : an introduction to programming

United Nations Statistical Commission & Economic Commission for europe — Statistical Data Editing: Impact on Data Quality

Naiyang Deng, Yingjie Tian, Chunhua Zhang — Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions

Allison Parrish, Ben Fry, Casey Reas — Make Getting Started with Processing.py

Anirudh Koul, Siddha Ganju, Meher Kasam — Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko — Kubeflow for Machine Learning: From Lab to Production

Hannes Hapke, Catherine Nelson — Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

Daniel Vaughan — Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise

Chris Fregly, Antje Barth — Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

Mark Treveil — Introducing MLOps: How to Scale Machine Learning in the Enterprise

Reese — Microcontrollers: From Assembly Language to C Using the PIC24 Family

Jacqueline Nolis, Emily Robinson — Build a Career in Data Science

Anderson, John — Hands On Machine Learning with Python: Concepts and Applications for Beginners

James D. Miller — Statistics for data science: leverage the power of statistics for data analysis, classification, regression, machine learning, and neural networks

Sibanjan Das, Umit Mert Cakmak — Hands-On Automated Machine Learning

Fausto Pedro Garcia Marquez (editor) — Advanced Multi-industry Applications of Big Data Clustering and Machine Learning

Φράσεις κλειδιά

Σχετικές λίστες βιβλίων