Building Machine Learning Solutions with TensorFlow | Path
Год выпуска: 2020
Производитель: Pluralsight
Сайт производителя:
//app.pluralsight.com/paths/skills/tensorflow
//app.pluralsight.com/paths/skills/building-machine-learning-solutions-with-tensorflow-20
Автор: Коллектив авторов
Продолжительность: 40h
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание:
TensorFlow is an open-source machine learning software library developed Google. Since it was released in 2015, it has become one of the most widely-used machine learning libraries. This skill will teach you how to implement the machine learning workflow using TensorFlow, and apply the library from Python to solve simple and complex machine learning problems.
Google released TensorFlow 2.0 in October 2019 which uses the dynamic graph and is more Python friendly. There are multiple changes to ensure removal of redundant APIs and better integration with Python runtime and Eager Execution.
What You Will Learn:
Design and implementation of machine learning solutions using TensorFlow 2.0
Applying Tensorflow to common analytical problems, such as classification, clustering, and regression
Debugging TensorFlow projects
Deploying TensorFlow projects to the cloud
Designing optimal Data pipelines
Applying TensorFlow to more advanced problems spaces, such as image recognition, language modeling, and predictive analytics
Prerequisites:
Python Programming
Machine Learning Literacy
Related Topics:
Statistics
Feature Engineering
Deep Learning
PyTorch
Содержание
Building Machine Learning Solutions with TensorFlow (2019)
A1. TensorFlow: Getting Started (Jerry Kurata, 2017)
A2. Understanding the Foundations of TensorFlow (Janani Ravi, 2017)
A3. Building Regression Models Using TensorFlow (Vitthal Srinivasan, 2017)
A4. Building Classification Models with TensorFlow (Janani Ravi, 2017)
A5. Building Unsupervised Learning Models with TensorFlow (Janani Ravi, 2017)
B1. Debugging and Monitoring TensorFlow Programs (Janani Ravi, 2018)
B2. Deploying TensorFlow Models to AWS, Azure, and the GCP (Janani Ravi, 2018)
C1. Language Modeling with Recurrent Neural Networks in TensorFlow (Janani Ravi, 2018)
C2. Implementing Image Recognition Systems with TensorFlow (Jon Flanders, 2019)
C3. Implementing Predictive Analytics with TensorFlow (Justin Flett, 2018)
C4. Sentiment Analysis with Recurrent Neural Networks in TensorFlow (Janani Ravi, 2017)
Building Machine Learning Solutions with TensorFlow 2.0 (2020)
A1. Getting Started with TensorFlow 2.0 (Janani Ravi, 2020)
A2. Installation Guide for TensorFlow 2.0 (Omotayo Aina, 2020) | Guide
B1. Designing Data Pipelines with TensorFlow 2.0 (Chase DeHan, 2020)
B2. Implement Hyperparameter Tuning for TensorFlow 2.0 (Gaurav Singhal, 2020) | Guide
B3. Building Machine Learning Solutions with TensorFlow.js (Abhishek Kumar, 2020)
C1. Build a Machine Learning Workflow with Keras TensorFlow 2.0 (Janani Ravi, 2020)
C2. Implement Time Series Analysis, Forecasting and Prediction with TensorFlow 2.0 (Chase DeHan, 2020)
Файлы примеров: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 30fps, 125 kb/s
Аудио: AAC, 44.1 kHz, 96 kbit/s, 2.0 chn
