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 ...
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 ...
Hey pupils! Welcome to the next session on modern neural networks. We are studying the basic neural networks that are revolutionizing different domains of life. In the previous session, we read the Deep Q Networks (DQN) Reinforcement Learning (add link). There, the basic concepts and applications were discussed in detail. Today, we will move towards another neural network, which is an improvement in the deep Q network and is named the double deep Q network.
In this article, we will point towards the basic workings of DQN as well so I recommend you read the deep Q networks if you don’t have a grip on this topic. We will introduce the DDQN in detail and will know the basic needs for improvement in the deep Q network. After that, we’ll discuss the history of these networks and learn ab ...
Hey pupil! Welcome to the next lecture on modern neural networks. I hope you are doing great. In the previous lecture, we saw the EffcientNet neural network, which is a convolutional Neural Network (CNN), and its properties. Today, we are talking about another CNN network called the capsule neural network, or CapsNets. These networks were introduced to provide the capsulation in CNNs to provide better functionalities.
In this article, we will start with the introduction of the capsule neural network. After that, we will compare these with the traditional convolutional neural networks and learn some basic applications of these networks. So, let’s start learning.
Introduction to Capsule Neural Networks
Capsule neural networks are a type of artificial neural network that was introduc ...
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 ...
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 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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Hello students, welcome to the second tutorial on deep learning in the first one, we have learned the simplest but basic introduction of deep learning to have a solid base about what we are actually going to do with deep learning. In the present lecture, we will take this to the advanced level and will learn the introduction with the intention of learning more and more about the introduction and understanding what we want to learn and how will we implement the concepts easily. So, here is a quick glance at the concepts that will be cleared today:
What do we mean by Deep learning?
What is the structure of calculation in neural networks?
How can you examine the Neural Networks?
What are some platforms of deep learning?
Why did we choose TensorFlow?
How can you work with TensorFlow?
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Hello peeps. Welcome to the next tutorial on deep learning. You have learned about the neural network, and it was an interesting way to compare different types of neural networks. Now, we are talking about deep learning frameworks. In the previous sessions, we introduced you to some important frameworks to let you know about the connection of different entities, but at this level, it is not enough. We are telling you in detail about all types of frameworks that are in style because of their latest features. So before we start, have a look at the list of concepts that will be covered today:
Introduction to the frameworks of deep learning.
Why do we require frameworks in deep learning?
What are some important deep learning frameworks?
What is TensorFlow and for which purpose of using Ten ...
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 ...
