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Machine Learning

Implementing Reinforcement Learning with Keras

In this tutorial, you will be learning how you can implement your own reinforcement learning tasks on Alibaba Cloud's Machine Learning Platform with Keras.

Fintechs: How to Capitalize on Your Data

From business intelligence to quantum computing, fintechs are now embracing a range of increasingly data-driven solutions to disrupt the market.

Natural Language Processing in Python 3 Using NLTK

This tutorial covers the basics of natural language processing (NLP) in Python by building a Named Entity Recognition (NER) using TF-IDF.

My Thoughts on Distributed Computing Frameworks

This article provides a fully verified solution (with code) to run LR and GBDT on a LibSVM-formatted dataset efficiently using TensorFlow.

Efficiently Use MERGE Statements for Your Data Projects

In this article, you'll learn what the MERGE statement is, why it is used, so that you can use it to efficiently insert or update data in your tables and databases.

DNN training for LibSVM-formatted data - From Keras to Estimator

This article provides a fully verified solution (with code) to run LR and GBDT on a LibSVM-formatted dataset efficiently using TensorFlow.

Alibaba Unveils AI Chip to Enhance Cloud Computing Power

At Apsara Conference 2019, Alibaba unveiled Hanguang 800, its first AI inference neural processing unit (NPU) that specializes in the acceleration of machine learning tasks.

Alibaba Open-Source and Lightweight Deep Learning Inference Engine - Mobile Neural Network (MNN)

Alibaba has made its lightweight mobile-side deep learning inference engine, Mobile Neural Network (MNN), open source to benefit more app and IoT developers.

6 Top AutoML Frameworks for Machine Learning Applications (May 2019)

In this post, we 6 key automated machine learning (AutoML) platforms that can assist data scientists to accelerate machine learning development.

Part 4: Image Classification using Features Extracted by Transfer Learning in Keras

In Part 4 of this 4-article series, we will load the saved model again for extracting features from the datasets.

Part 3: Image Classification using Features Extracted by Transfer Learning in Keras

In Part 3 of this 4-article series, we are going to transfer the learning of MobileNet for working with the Fruits360 dataset.

Part 2: Image Classification using Features Extracted by Transfer Learning in Keras

In Part 2 of this 4-article series, we will create a Jupyter notebook and download the Fruits360 dataset using Keras within the Jupyter notebook.

Part 1: Image Classification using Features Extracted by Transfer Learning in Keras

In Part 1 of this 4-article series, we will explore the ML pipeline to highlight the challenges of manual feature extraction.

Finding Public Data for Your Machine Learning Pipelines

This article discusses how and where you can find public data to use in machine learning pipelines that you can then use in a variety of applications.

The Wild, Wild Apache Flink: Challenges and Opportunities

This blog article discusses how Apache Flink and its ecosystem may be on the verge of something great in the machine learning space, despite many challenges.

Revealing the Dark Magic Behind Deep Learning

In this blog, we'll review useful tricks in deep learning and compile an optimization scheme called "checklist testing" that can upgrade any existing deep learning repository.

Almost Human: Alibaba AI Ranks First in Visual Dialog Challenge

In this article, we will highlight Alibaba's Artificial Intelligence (AI) team's achievement in the recent Visual Dialog Challenge – a feat comparable to humans.

Machine Learning Algorithms and Scikit-Learn

This tutorial looks at the Scikit-Learn library for machine learning and how you can use machine learning algorithms on Alibaba Cloud.

A Closer Look into the Major Types of Machine Learning Models

This article takes a closer look into what are the major types of machine learning models and how these models are implemented.

Data Preprocessing for Machine Learning

This tutorial discusses the preprocessing aspect of the machine learning, including specific techniques, and a simple way in which you can implement these techniques.