BOOKS - HUMANITIES - E-learning как сделать электронное обучение понятным, качественн...
E-learning как сделать электронное обучение понятным, качественным и доступным - Майкл Аллен 2020 PDF Альпина BOOKS HUMANITIES
ECO~22 kg CO²

3 TON

Views
4656

Telegram
 
E-learning как сделать электронное обучение понятным, качественным и доступным
Author: Майкл Аллен
Year: 2020
Format: PDF
File size: 19 MB
Language: RU



Pay with Telegram STARS
''

You may also be interested in:

Machine Learning Master Machine Learning Fundamentals for Beginners, Business Leaders and Aspiring Data Scientists
Learning PyTorch 2.0, Second Edition Utilize PyTorch 2.3 and CUDA 12 to experiment neural networks and Deep Learning models
Personality as a Factor Affecting the Use of Language Learning Strategies: The Case of University Students (Second Language Learning and Teaching)
Learning PyTorch 2.0: Experiment deep learning from basics to complex models using every potential capability of Pythonic PyTorch
Deep Learning for the Life Sciences Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More First Edition
Learning PyTorch 2.0 Experiment Deep Learning from basics to complex models using every potential capability of Pythonic PyTorch
Differing visions of a Learning Society Vol 2: Research findings Volume 2 (ESRC Learning Society series)
Getting started with Deep Learning for Natural Language Processing Learn how to build NLP applications with Deep Learning
Online Machine Learning: A Practical Guide with Examples in Python (Machine Learning: Foundations, Methodologies, and Applications)
Learning PyTorch 2.0, Second Edition Utilize PyTorch 2.3 and CUDA 12 to experiment neural networks and Deep Learning models
Learning PyTorch 2.0 Experiment Deep Learning from basics to complex models using every potential capability of Pythonic PyTorch
Building Scalable Deep Learning Pipelines on AWS Develop, Train, and Deploy Deep Learning Models
Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series)
Reinforcement Learning with TensorFlow: A beginner|s guide to designing self-learning systems with TensorFlow and OpenAI Gym
Learning Pandas 2.0: A Comprehensive Guide to Data Manipulation and Analysis for Data Scientists and Machine Learning Professionals
Ступеньки юного пианиста. Пособие для начинающих обучение игре на фортепиано
Прикладное машинное обучение с помощью Scikit-Learn, Keras и TensorFlow 2-е издание
Википедия и YouTube для всех досуг и развлечения, справочники и обучение, бизнес
Machine Learning For Beginners Guide Algorithms Supervised & Unsupervsied Learning. Decision Tree & Random Forest Introduction
Deep Learning fur die Biowissenschaften Einsatz von Deep Learning in Genomik, Biophysik, Mikroskopie und medizinischer Analyse
Machine Learning The Ultimate Guide to Understand AI Big Data Analytics and the Machine Learning’s Building Block Application in Modern Life
Machine Learning with Core ML 2 and Swift A beginner-friendly guide to integrating machine learning into your apps
Machine Learning: A Guide to PyTorch, TensorFlow, and Scikit-Learn: Mastering Machine Learning With Python
Deep Learning for Data Architects Unleash the power of Python|s deep learning algorithms
Deep Learning for Data Architects Unleash the power of Python|s deep learning algorithms
Deep Learning for the Life Sciences Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python
Deep Learning for Finance Creating Machine & Deep Learning Models for Trading in Python
Learning Google Cloud Vertex AI Build, deploy, and manage machine learning models with Vertex AI
Machine Learning for Beginners Build and deploy Machine Learning systems using Python, 2nd Edition
Learning Google Cloud Vertex AI Build, deploy, and manage machine learning models with Vertex AI
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python
Active Machine Learning with Python: Refine and elevate data quality over quantity with active learning
Anatomy of Deep Learning Principles: Writing a deep learning library from scratch (Japanese Edition)
Deep Learning for Finance Creating Machine & Deep Learning Models for Trading in Python
Human-in-the-Loop Machine Learning Active learning, annotation and human-computer interaction (MEAP)
Robust Machine Learning: Distributed Methods for Safe AI (Machine Learning: Foundations, Methodologies, and Applications)
Learning to Love (The Learning Trilogy #3)
Learning Race, Learning Place
Learning to Move Forward (Learning, #3.5)