Category: Deep Learning
Day 1: Introduction to Deep Learning: Understanding the Basics
Welcome to this Deep Learning Course! Today is the beginning of our course and in this first article we will be talking about basics of Deep Learning. So, Let’s get started. Deep learning, is a subfield of machine learning that focuses on algorithms inspired by the structure of the human brain called which is an…
Read MoreMastering Keras: Best Practices for Optimizing Your Python Models
The popularity of Keras, a Python library for building and training deep learning models, has increased as deep learning evolves. It is known for its user-friendly API, which allows developers to quickly build, test, and deploy deep learning models. This article will cover the best practices for optimizing Keras models, including understanding Python Keras libraries,…
Read MoreEssential Keras Functions for Python Programmers
Python developers are likely to be familiar with constructing neural networks for deep learning applications. Although Python is a potent language that enables the development of complex applications, deep learning can be difficult. This is where Keras comes into play. Keras is a sophisticated neural network API that is coded in Python. It was created…
Read MorePyTorch vs. TensorFlow: A Comprehensive Comparison
As the world of artificial intelligence (AI) continues to grow, so does the demand for deep learning frameworks. PyTorch and TensorFlow are two of the most popular deep learning frameworks available today. In this article, we will be providing you a comprehensive comparison between PyTorch and TensorFlow to help you make the right choice for…
Read More10 Common Data Science Interview Questions and How to Answer Them?
Data science has become a very competitive field and it is important to prepare for data science interviews if you are looking for your dream job. As part of the interview process, you can expect to be asked a number of questions to assess your knowledge, skills and experience in the field. In this blog…
Read More10 Tips for Building Machine Learning Models for TensorFlow
Building a machine learning model using TensorFlow can be a daunting task, but it doesn’t have to be. Here are ten tips for building a successful machine learning model with TensorFlow. 1 – Preprocess Your Data: Preprocessing is essential in machine learning. Before feeding the data to a model, it is necessary to preprocess it…
Read More10 Tips for Building Machine Learning Models with PyTorch
Machine learning is a rapidly growing field, and PyTorch is one of the most popular frameworks for building machine learning models. It is an open-source machine learning library based on the Torch library written in Python. With its easy-to-use interface and excellent GPU acceleration support, PyTorch has become the choice of many researchers and developers.…
Read MoreHow to use Generative Adversarial Networks in Machine Learning?
Generative Adversarial Networks (GANs) are a type of simple machine learning algorithmic rule that has gained significant attention in recent years. GANs can generate recently data that resembles the original dataset, which makes them ideal for tasks so much as image and speech generation. If you’re interested in learning how to use GANs in your…
Read More10 Tips for Training Deep Learning Models
Deep learning models have made significant impact in fields ranging from computer vision to natural language processing. However, training these models can be a daunting task that requires a lot of knowledge and expertise. In this blog, we will see 10 tips for training sustainable deep learning models. 1 – Start with a small dataset:…
Read More3 Important Neural Network Architectures Explained
1. Perceptron The perceptron is the most basic of all neural networks, being a fundamental building block of more complex neural network. If simple connects an input cell and an output cell. 2. Feed-Forward Network The feed-forward network is a collection of perceptions’. In which there are three fundamental types of layers – input layers,…
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