Category: Deep Learning
How to Become a Prompt Engineer in 2024
- Naveen
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In this article, we will explore the role of prompt engineers in the field of AI technology. Prompt engineers play a crucial role in enabling AI models to generate relevant output based on user input. We will discuss the basics of prompt engineering, its importance, and the steps and skills required to become a proficient…
Read MoreHow to Create an Image Classification Model using Hugging Face in 5 Lines
- Naveen
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In 2021, when it comes to natural language processing tasks, most people turn to Hugging Face for solutions. However, did you know that Hugging Face now also offers image-related solutions? Yes, you heard it right! The popular Transformers library can now help you classify images as well. In this blog post, I will show you…
Read MoreBuilding a Classification Model with VGG19 for Image Recognition
- Naveen
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Image classification is a fascinating field of machine learning that involves teaching a computer to recognize and categorize objects or patterns within images. In this article, we will walk through the process of building a classification model using the VGG19 architecture for image recognition. We’ll start from importing the necessary libraries and proceed step by…
Read MoreGenerating new MNIST digits in PyTorch using AutoEncoder
- Naveen
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Are you curious about how machines can learn to understand and generate images? In this blog post, we’re going to delve into the fascinating world of Autoencoders using PyTorch, and we’ll explain this concept in simple terms. Autoencoders are a type of artificial neural network that can compress data and then reconstruct it. This article…
Read MoreTop 10 Best AI Companies in 2024
- Naveen
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In our fast-changing world, new ideas are really important for making progress. One big idea that’s changing things a lot is called artificial intelligence, or AI for short. AI isn’t just something for the future; it’s already making big changes in the world, and it’s doing it with a level of accuracy that used to…
Read MoreDenoising Images with Autoencoders Using TensorFlow and Python
- Naveen
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In today’s digital world, images play an important role in various applications, from medical imaging to self-driving cars. However, images are often corrupted by noise during transmission or storage, which can hinder the performance of image processing algorithms. In this blog post, we will explore how to use autoencoders to denoise images. We will implement…
Read More10 Underrated AI Tools That Will Change Your Life and Business
- Naveen
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In this blog post, we will explore ten underrated and less-known AI tools that have the potential to revolutionize your life and business. These tools cover a wide range of functionalities, from creating customized QR codes to competitor research, photo editing, podcast note-taking, meal planning, essay writing, video summarization, lead magnet generation, article summarization, and…
Read MoreBackpropagation in Neural Networks with an Examples
- Naveen
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In this article, we will talk about the concept of backpropagation, which can be considered the building block of a neural network. After reading this article, you will understand why backpropagation is important and why it is applied in various fields. What is Back Propagation? Back propagation is an algorithm created to test errors that…
Read MoreDay 7 – What Are GANs? | Generative Adversarial Networks in Deep Learning
- Naveen
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In this article, we will explore an important and popular deep learning neural network called Generative Adversarial Networks (GANs). GANs were introduced in 2014 by Ian J. Goodfellow and co-authors and have since become very popular in the field of machine learning. GANs are an unsupervised learning task that consists of two models, the generator…
Read MoreDay 6 – What is Loss Function in Deep Learning | Loss Function in Machine Learning | Loss Function Types
- Naveen
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In this blog, we will cover the concept of a loss function and its significance in artificial neural networks. Loss functions play a crucial role in model training, as they are used by stochastic gradient descent to minimize the error during the training process. We will discuss how loss functions are calculated and their importance…
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