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 ...
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 ...
Hey buddies! Welcome to the next tutorial on deep learning, in which you are about to acquire knowledge related to Python. This is going to be very interesting because the connection between these two is easy and useful. In the last lecture, we had an eye on the latest and trendiest deep learning algorithms, and therefore, I think you are ready to take the next step towards the implementation of the information that I shared with you. To help you make up your mind about the topics of today, I have made a list for you that will surely be useful for you to understand what we are going to do today.
How do you introduce the Python programming language to a deep learning developer?
How is Python useful for deep learning training in different ways?
Do Python provide the useful frameworks for ...
Hello pupils! Welcome to the next section of neural network training. We have been studying modern neural networks in detail, and today we are moving towards the next neural network, which is the Echo State Network (ESN). It is a type of recurrent neural network and is famous because of its simplicity and effectiveness.
In this tutorial, we’ll start learning with the basic introduction of echo state networks. After that, we’ll see the basic concepts that will help us to understand the work of these networks. Just after this, we’ll see the steps involved in setting the ESNs. In the end, we’ll see te fields where ESNs are extensively used. Let’s start with the first topic:
Introduction to Echo State Networks (ESNs)
The echo state networks ( ...
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 ...
Hi there! I hope you are having a great day. The success of the field of deep learning is due to its complex and advanced neural networks. These networks can be broadly divided into traditional and modern neural networks. We have seen the details of traditional neural networks, and in the previous session, the basic introduction of modern neural networks and the details of their features were discussed. Today, we will talk about one of the most famous modern neural networks, the Kohonen Self-Organized Neural Network.
Modern neural networks are more organized and developed than traditional neural networks, but that does not make traditional neural networks less efficient than modern ones. All the networks are introduced for specific tasks, and this is one of the main reasons behind t ...
Hi readers! I hope you’re having a great day and finding something thrilling. Imagine being able to solve a problem in seconds that would take the fastest supercomputers millennia, that is, quantum computing. Today, we will cover Quantum Computing.
Quantum computing is a relatively new technology that can present a new way of thinking about how information may be processed using the laws of quantum mechanics. Classical computing uses bits, which are either 0 or 1, while processing information, whereas quantum computing uses qubits and has the possibility of being a bunch of things at the same time by virtue known as the “superposition”. In addition to "superposition", qubits can be connected across space through a property known as "Entanglement", which allows quantum computers the potent ...
Hello Peeps! Welcome to the next lecture on deep learning, where we are discussing TensorFlow in detail. You have seen why we have chosen TensorFlow for this course, and we have read a lot about the working mechanism, programming languages, and advantages of using TensorFlow instead of other libraries. Instead of using the other options for the same purpose, we have seen several reasons to use TensorFlow. Because of the latest work on the library for more improvement and better results, it's now time to learn the specifics of TensorFlow installation. But before this, you have to check the list of the concepts that will be cleared today:
Is Installation of TensorFlow Difficult?
The simple and to-the-point answer to this question is, the installation is easy and usually does not require ...
Hello Learners! Welcome to the next lecture on deep learning. We have read the detailed introduction to deep learning and are moving forward with the introduction of the neural network. I am excited to tell you about the neural network because of the interesting and fantastic applications of neural networks in real life. Here are the topics of today that will be covered in this lecture:
What do we mean by the neural network?
How can we know about the structure of the neural network?
What are the basic types of neural networks?
What are some applications of these networks?
Give an example of a case where we are implementing neural networks.
Artificial intelligence has numerous features that make it special and magical in different ways, and we will be exploring many of them in dif ...
