This article we will walk you through and compare the code usability and ease to use of TensorFlow and PyTorch on the most widely used MNIST dataset to classify handwritten digits.
As both are popular choices when it comes to ML model deployment, let’s look at how they work and what makes them different from each other
Google’s LinMaxMatch approach improves performance, makes computation faster and reduces complexity
ktrain is a lightweight wrapper library for TensorFlow Keras. It can be very helpful in building projects consisting of neural networks. Using this wrapper, we can build, train and deploy deep learning and machine learning models.
There are many cases where we get the requirements of probabilistic models and techniques in neural networks. These requirements can be filled up by adding probability layers to the network that are provided by TensorFlow.
With six years passing by since its initial release in 2015, let us look back at TensorFlow’s journey
In the previous version, TensorFlow error stack traces involved many internal frames, which could be challenging to read through.
DermAssist performs on-device image quality inspections using TensorFlow.js.
In scalable machine learning, we try to build a system where the components of the system have their own work or task which helps the whole system to lead towards the solution of the problem rapidly
TensorFlow Recommenders (TFRS) is an open-source TensorFlow package that simplifies the building, evaluation, and deployment of advanced recommender models.
TensorFlow Similarity looks to support semi-supervised and self-supervised learning techniques in the coming months.
Tensorflow has used a statistical 3D human body called GHUM, which is developed using a large corpus of human shapes and motions.
Sentiment analysis is a part of natural language processing used to determine whether the sentiment of the data under observation is positive, negative or neutral. Usually, sentiment analysis is carried on text data to help professionals monitor and understand their brand and product sentiment across the industry and customers by taking the feedback.
TensorFlow allows developers to create dataflow graphs, which are structures that describe how the data moves through a graph, or a series of processing nodes present.
Distributed training in TensorFlow is built around data parallelism, where we can replicate the same model architecture on multiple devices and run different slices of input data on them. Here the device is nothing but a unit of CPU + GPU or separate units of GPUs and TPUs. This method follows like; our entire data is divided into equal numbers of slices. These slices are decided based on available devices to train; following each slice, there is a model to train on that slice.
Search and recommendation systems have been the most popular applications of LTR models.
JAX is a Python library designed for high-performance numerical computing.
The recent advancement in deep learning models has been largely attributed to the quantity and diversity of data gathered…
Representation learning is a machine learning (ML) method that trains a model to discover prominent features. It may apply to a wide range of downstream
Data science deals with multiple formats of data. At the basic level, we start with the CSV and Excel files. As we dig deeper into
TensorFlow Certification is a testament to developers’ expertise in machine learning. The certification programme essentially consists of an assessment examination developed by the TensorFlow team.
“MXNet, born and bred here at CMU, is the most scalable framework for deep learning I have seen and is a great example of what
TensorFlow Serving is an easy-to-deploy, flexible and high performing serving system for machine learning models built for production environments. It allows easy deployment of algorithms
Google’s TensorFlow and Facebook’s PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow’s
Sentiment Analysis is a text classification application in which a given text is classified into either a positive class or a negative class
Image Generation is one of the most curious applications in Computer Vision. Variational Autoencoders and GANs are the preferred base models
TensorFlow Lite has emerged as a popular platform for running machine learning models on the edge. A microcontroller is a tiny low-cost device to perform
After version 2.4, the Google Brain team has now released the upgraded version of TensorFlow, version 2.5.0. The latest version comes with several new and
Object detection is the process of classifying and locating objects in an image using a deep learning model. Object detection is a crucial task in
Semantic segmentation in computer vision is the supervised process of pixel-level image classification into two or more Object classes
Join the forefront of data innovation at the Data Engineering Summit 2024 where industry leaders redefine technology 8217 s future
© Analytics India Magazine Pvt Ltd & AIM Media House LLC 2024
The Belamy, our weekly Newsletter is a rage. Just enter your email below.
