Hello friends, I hope you all are having fun. Today, we are bringing you one of the most advanced and trending courses named "Deep Learning". Today, I am sharing the first tutorial, so we will discuss the basic Introduction to Deep Learning, and in my upcoming lectures, we will explore complex concepts related to it. Deep Learning has an extensive range of applications and trends and is normally used in advanced research. So, no matter which field you belong to, you can easily understand all the details with simple reading and practicing. So without any further delay, let me show you the topics that we are going to cover today:What is deep learning?What are artificial intelligence and machine learning?Working with deep learning using neural networks.Trends in deep learning.Deep learning as ...
Hello readers! Welcome to the next episode of the Deep Learning Algorithm. We are studying modern neural networks and today we will see the details of a reinforcement learning algorithm named Deep Q networks or, in short, DQN. This is one of the popular modern neural networks that combines deep learning and the principles of Q learning and provides complex control policies.Today, we are studying the basic introduction of deep Q Networks. For this, we have to understand the basic concepts that are reinforcement learning and Q learning. After that, we’ll understand how these two collectively are used in an effective neural network. In the end, we’ll discuss how DQN is extensively used in different fields of daily life. Let’s start with the basic concepts.
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Hi readers! Hopefully, you are doing well and exploring something fascinating and advanced. Imagine that particles can pass through walls but not by breaking them down? Yes, it is possible. Today, we will study Quantum Tunneling.
Quantum tunneling may be one of the strangest and illogical concepts of quantum mechanics. Quantum Tunneling proves the phenomenon of particles like electrons, protons, or even whole atoms percolating through the energy barrier of potential energy, although they do not appear to have sufficient potential to slide over it. The classical physics version of this ball at this point would merely reverse.
Nevertheless, in the quantum realm of things, particles now act like waves, and waves can pass through and even over barriers with some nonzero probability of the pa ...
Deep learning is an important subfield of artificial intelligence and we have been working on the modern neural network in our previous tutorials. Today, we are learning the transformer architecture neural network in deep learning. These neural networks have been gaining popularity because they have been used in multiple fields of artificial intelligence and related applications.
In this article, we will discuss the basic introduction of TNNs and will learn about the encoder and decoders in the structure of TNNs. After that, we will see some important features and applications of this neural network. So let’s get started.
What are Transformer Neural Networks
Transformer neural networks (TNNs) were first introduced in 2017. Vaswani et al. h ...
Hello pupils! Welcome to the next session of the neural network series. I hope you are doing good. In the previous part of this series, I showed the double deep Q networks and discussed their differences from the deep Q network to make things clear. Today, I am going to visit a very popular neural network with you. This is the spiking neural network that mimics the functionality of the biological neurons with the help of spikes. This is a different neural network than the traditional networks and you will see the details of each point.
In this lecture, we’ll understand the introduction of the spiking neural network. We’ll discuss all the basic terms that are used while studying the SNN. After that, we’ll move on to the steps of using SNN in detail. In the end, we’ll move towards the appl ...
Hello pupils! Welcome to the following lecture on deep learning. As we move forward, we are learning about many of the latest and trendiest tools and techniques, and this course is becoming more interesting. In the previous lecture, you saw some important frameworks in deep learning, and this time, I am here to introduce you to some fantastic algorithms of deep learning that are not only important to understand before going into the practical implementation of the deep learning frameworks but are also interesting to understand the applications of deep learning and related fields. So, get ready to learn the magical algorithms that are making deep learning so effective and cool. Yet before going into details, let me discuss the questions for which we are trying to find answers.
How does dee ...
Hey readers! Welcome to the next episode of training on neural networks. We have been studying multiple modern neural networks and today we’ll talk about autoencoders. Along with data compression and feature extraction, autoencoders are extensively used in different fields. Today, we’ll understand the multiple features of these neural networks to understand their importance.In this tutorial, we’ll start learning with the introduction of autoencoders. After that, we’ll go through the basic concept to understand the features of autoencoders. We’ll also see the step by step by step process of autoencoders and in the end, we’ll see the model types of autoencoders. Let’s rush towards the first topic:
What are Autoencoders?
Autoencoders are the type of neural networks that are used to lear ...
Hi readers! I hope you are doing great. We are learning about modern neural networks in deep learning, and in the previous lecture, we saw the capsule neural networks that work with the help of a group of neurons in the form of capsules. Today we will discuss the graph neural network in detail.
Graph neural networks are one of the most basic and trending networks, and a lot of research has been done on them. As a result, there are multiple types of GNNs, and the architecture of these networks is a little bit more complex than the other networks. We will start the discussion with the introduction of GNN.
Introduction to Graph Neural Networks
The work on graphical neural networks started in the 2000s when researchers explored graph-based semi-supervised learning in the neural network. The ...
Deep learning has applications in multiple industries, and this has made it an important and attractive topic for researchers. The interest of researchers has resulted in multiple types of neural networks we have been discussing in this series so far. Today, we are talking about generative advertising neural networks (GAN). This algorithm performs the unsupervised learning task and is used in different fields of life such as education, medicine, computer vision, natural language processing (NLP), etc.
In this article, we will discuss the basic introduction of GAN and will see the working mechanism of this neural network, After that, we will see some important applications of GANs and discuss some real-life examples to understand the concept. So let’s move towards the introduction of GANs ...
Hello! I hope you are doing great. Today, we will talk about another modern neural network named gated recurrent units. It is a type of recurrent neural network (RNN) architecture but is designed to deal with some limitations of the architecture so it is a better version of these. We know that modern neural networks are designed to deal with the current applications of real life; therefore, understanding these networks has a great scope. There is a relationship between gated recurrent units and Long Short-Term Memory (LSTM) networks, which has also been discussed before in this series. Hence, I highly recommend you read these two articles so you may have a quick understanding of the concepts.
In this article, we will discuss the basic introduction of gated recurrent units. It is better ...
