A hands on tutorial that covers how to train a machine learning model using the Reddit comment moderation dataset, and deploy it in a scalable infrastructure using Kafka and Seldon Core Read More
#python, #devopsTag Archives: Python
DeepSpeed: Extreme-scale model training for everyone
In February, we announced DeepSpeed, an open-source deep learning training optimization library, and ZeRO (Zero Redundancy Optimizer), a novel memory optimization technology in the library, which vastly advances large model training by improving scale, speed, cost, and usability.
… Today, we are happy to share our new advancements that not only push deep learning training to the extreme, but also democratize it for more people—from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. More specifically, DeepSpeed adds four new system technologies that further the AI at Scale initiative to innovate across Microsoft’s AI products and platforms. These offer extreme compute, memory, and communication efficiency, and they power model training with billions to trillions of parameters. Read More
4 Python AutoML Libraries Every Data Scientist Should Know
Automated Machine Learning, often abbreviated as AutoML, is an emerging field in which the process of building machine learning models to model data is automated. AutoML has the capability to make modelling easier and more accessible for everyone.
If you’re interested in checking out AutoML, these four Python libraries are the way to go. Read More
A Very Simple Introduction to Deep Learning on Amazon Sagemaker
Dive into Deep Learning
This book represents our attempt to make deep learning approachable, teaching you the concepts,the context, and the code. The book teaches most concepts just in time, interleaving runnable code with the material.
Learn more: https://amazon.science/latest-news/amazon-scientists-author-popular-deep-learning-book
★★★★FREE Download: https://d2l.ai/d2l-en.pdf
Adventures in PyTorch — Image classification with CalTech Birds 200 — Introduction
This series will explore the power of Facebook AI Research’s (FAIR) powerful neural network and machine learning architecture, PyTorch. In this series of articles, we will explore the power of PyTorch in application to an image classification problem, to identify 200 species of North American bird using the CalTech 200 birds dataset, by using various CNN architectures including GoogLeNet, ResNet152 and ResNeXt101, among others. Read More
COVID Mask Detection using Machine Learning
A Deep Dive into Reinforcement Learning
Let’s take a deep dive into reinforcement learning. In this article, we will tackle a concrete problem with modern libraries such as TensorFlow, TensorBoard, Keras, and OpenAI gym. You will see how to implement one of the fundamental algorithms called deep Q-learning to learn its inner workings. Regarding the hardware, the whole code will work on a typical PC and use all found CPU cores (this is handled out of the box by TensorFlow). Read More
YOLO Object Detection
YOU ONLY LOOK ONCE tutorial where you can learn about the YOLO Object Detection system, and how to implement such a system with Keras. Read More
Top 10 Courses to Learn AI, Machine Learning and Deep Learning
Supervised, semi-supervised or unsupervised deep learning is part of a broader family of machine learning methods, that teach you the basics of neural networks. Learn from the Top 10 Deep Learning Courses curated exclusively by Analytics Insight and build your deep learning models with Python and NumPy. Read More
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