Backpropagation in Neural Networks with an Examples
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 MoreTop 10 AI Tools in 2023 That Will Make Your Life Easier
In this article, we’ll explore ten remarkable AI tools that are revolutionizing various industries. These tools are designed to simplify tasks, enhance workflow, and boost productivity. The tools featured in our list are among the most advanced and widely used in the market, applicable to a range of purposes. Some focus on natural language processing,…
Read MoreHow to use isinstance() Function in Python | isinstance() in Python
In this article, we will explore the isinstance() function, which is a built-in function in Python. This function is commonly used by professionals to compare two different data types and determine whether they are the same or not. By using isinstance(), we can easily check if a variable is of a specific data type before…
Read MoreDay 7 – What Are GANs? | Generative Adversarial Networks in Deep Learning
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 MoreDeque : Memory Efficient Alternative To Python Lists
In this blog, we will be covering deque, which stands for Double Ended Queue in Python. We will explore why this data structure is very useful, especially when managing a stack in Python. We will go over the methods that come with the Double Ended Queue and how we can use it to handle queues…
Read MoreDay 6 – What is Loss Function in Deep Learning | Loss Function in Machine Learning | Loss Function Types
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…
Read More11 Tips to Instantly Improve Your Python Code
Python is a powerful programming language known for its simplicity and readability. In this article, we will explore 11 tips that can instantly improve your Python code. These tips include best practices that make your code cleaner and more pythonic. Tip 1: Iterate with `enumerate` instead of `range(len())` When you need to iterate over a…
Read MoreUnderstanding Principal Component Analysis in Machine Learning
In our fast-paced world, data grows more complex each day and, by extension, more challenging to interpret. In machine learning, we use a mathematical technique called Principal Component Analysis (PCA) to simplify our data —that is, reduce features or dimensions while trying to maintain as much information as possible. Why PCA Matters? There are various…
Read MoreWhat is Feature Engineering in Machine Learning | Feature Engineering Techniques
If you are into machine learning, then you probably know that feature engineering is an important step in building a machine-learning model that actually works. Feature engineering is the process of transforming existing features or creating new features to improve the performance of a machine-learning model. Feature engineering is the process of taking raw data…
Read MoreLeNet-5 Architecture Explained | Introduction to LeNet-5 Architecture
LeNet-5 is a compact neural network comprising fundamental components of deep learning convolutional layers, pooling layers, and fully connected layers. It serves as a foundational model for other deep learning architectures. Let’s talk about the LeNet-5 and enhance our understanding of convolutional and pooling layers through practical examples. Introduction to LeNet-5 LeNet-5 consists of seven…
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