Machine Learning

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Let us know what is TensorFlow Lite Task Library

Reading Time: 2 minutes TensorFlow Lite is a framework of software packages that enables ML training locally on the hardware. This on-device processing and computing allow developers to run their models on targeted hardware. The hardware includes development boards, hardware modules, and embedded and IoT devices. TensorFlow Lite Task Library contains a useful and powerful set of interfaces. That helps us handle most of the pre-processing and post-processing logic Continue Reading

Let’s know how to Convert the TensorFlow model to the TensorFlow Lite model

Reading Time: 2 minutes TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded devices. It allows you to run machine learning models on edge devices with low latency, eliminating the need for a server. After the development of the TensorFlow model, we can convert the same to a more efficient and smaller version by converting it into a Tflite model format. Let’s explore how. Conversion Workflow of TensorFlow Lite Continue Reading

Best Approach For Resume screening by Machine Learning-Part 1

Reading Time: 3 minutes Introduction Resume screening is the process of determining whether a candidate is qualified for a role based on his or her education, experience, and other information captured on their resume. It’s a form of pattern matching between a job’s requirements and the qualifications of a candidate based on their resume. The goal of screening resumes is to decide whether to move a candidate forward – Continue Reading

Why Computer Vision Had Been So Popular Till Now?

Reading Time: 3 minutes What is computer vision? Computer vision(CV) is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images or videos) in the same way that humans do. The concept of computer vision is based on teaching computers to process an image at a pixel level and understand it. Technically, machines attempt to retrieve visual Continue Reading

Kubeflow: A Complete Solution to MLOps.

Reading Time: 5 minutes Hey Folks, In this blog we are going to put some light on Kubeflow, an open-source platform that enables us to orchestrate complicated workflows running on Kubernetes of machine learning pipelines. Kubeflow Emerges for ML Workflow Automation Many data scientists today find it burdensome to manually execute all of the steps in a machine learning workflow. Moving and transforming data, training models, then promoting them Continue Reading

Is TensorFlow a good fit for model optimisation?

Reading Time: 2 minutes In the realm of AI, a great deal of consideration is optimising training. There is a lot less information out there on optimising models. However serving models for forecast is the place where we make money out of ML. Without a doubt, the expense of serving forecasts might be a central point in the all out profit from speculation for a ML application. In this Continue Reading

TensorFlow Recommenders (TFRS): An Overview

Reading Time: 4 minutes Hey Guys, Aren’t you surprised, when you watch any video on youtube or any movie on Netflix or look for any product on an E-Commerce website?You start receiving similar kinds of videos, movies, and products suggestion on respective platforms.So, how do platforms do that?.Well, they use recommender systems, an important application of machine learning, surfacing new discoveries and helping users find what they love.In this Continue Reading

Let us know about TensorFlow Extended (TFX) components and Libraries?

Reading Time: 3 minutes In this blog, we will be learning about Tensorflow Extended (TFX) components and libraries. TFX is a Google-production-scale machine learning (ML) platform based on TensorFlow. It provides a configuration framework and shared libraries. Moreover, to integrate common components needed to define, launch, and monitor your machine learning system. How Tensorflow Extended (TFX) came up? Since the time Google has publicized Tensorflow, its application in Deep Continue Reading

Fundamentals Of Classification Models Part-2

Reading Time: 3 minutes This article is the continuation of “Fundamentals of Classification Models Part – 1” You need to go through this part before going to learn about Classifier Models. Classifier Models As discussed in the previous article ” We prepare the data for training the algorithm” the first step is to pre-process and clean the data The cleaning we need for this dataset is to change the Continue Reading

Convolutional Neural Network in TensorFlow

Reading Time: 3 minutes Introduction: As you might know, Neural networks reflect the behaviour of the human brain, allowing computer programs to recognise patterns and solve common problems in the fields of AI, machine learning, and deep learning. Neural networks are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has Continue Reading

Google BigQuery: An Introduction to Big Data Analytics Platform.

Reading Time: 6 minutes Hey Folks, Today we going to discuss Google BigQuery, an enterprise data warehouse with built-in machine learning capabilities. Before going to BigQuery, let’s understand what is Google Cloud Platform?Google Cloud Platform is a suite of public cloud computing services offered by Google. The platform includes a range of hosted services for compute, storage and application development that run on Google hardware. Google Cloud protects your data, applications, Continue Reading

How To Find Correlation Value Of Categorical Variables.

Reading Time: 4 minutes Hey folks, In this blog we are going to find out the correlation of categorical variables. What is Categorical Variable? In statistics, a categorical variable has two or more categories.But there is no intrinsic ordering to the categories. For example, a binary variable(such as yes/no question) is a categorical variable having two categories (yes or no), and there is no intrinsic ordering to the categories. Continue Reading

Dealing with Missing Values in Python

Reading Time: 4 minutes For any Data Scientist, its very normal to deal with data sets having missing terms and still be able to manage and create a good predictive model out of it. Here we will discuss some techniques to handle missing data in a given data set. Missing Value occur when no data is stored for a variable or feature. It could be represented as “?”, “NA”, Continue Reading