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Last updated: Feb 7, 2022

Deep Learning

Deep learning is a more advanced methodology than Machine Learning, which encompasses supervised, unsupervised, and recommendation systems. It is made up of various algorithms and neural networks, such as Convolution Neural Network (CNN) and Recurrent Neural Network (RNN), which have a wide range of real-world applications.
ANN vs CNN vs RNN
The objective of this blog is to understand the difference between ANN vs. CNN vs. RNN
Deep Learning vs Supervised Learning MEDIUM
This article covers the concept of deep learning and supervised learning and their differences.
Deep Learning vs Neural Networks MEDIUM
This article will discuss the differences between Deep learning and Neural networks.
Artificial Intelligence in Education EASY
In this blog, we will see the different ways in which AI is transforming education. We will explore its benefits and challenges.
Author Tisha
0 upvotes
Deep Learning Frameworks Comparison
In this blog, we will discuss various types of frameworks in deep learning, features, flexibility, and framework comparison.
Features of Artificial Intelligence EASY
In this blog, we will learn about Features of Artificial Intelligence. Get ready to understand how AI is shaping our world & what makes it so powerful. .
Deep Learning vs NLP MEDIUM
In this article, we will discuss what deep learning and Natural Language Processing (NLP) are, along with various differences between them.
Artificial Intelligence Books EASY
In this blog, we will explore about Artificial Intelligence Books.
Deep Learning vs Computer Vision MEDIUM
In this article, we will discuss what artificial intelligence, deep learning, and Computer Vision are, their importance, and various differences between them.
Deep Learning vs Reinforcement Learning MEDIUM
This article covers the concept of deep learning and reinforcement learning and their differences.
Deep Learning vs Data Science
In this blog, we will discuss Deep Learning vs Data Science and explore various use cases of Deep Learning and Data Science.
Deep Learning vs CNN EASY
This blog will discuss the topic of Deep Learning vs CNN, including the definition, features, pros and cons.
Image Captioning MEDIUM
In this article, we will understand Image captioning. We will see its working, types of architectures, phases, and applications.
Image Caption Generator MEDIUM
This article will teach you how to build an image caption generator.
Fuzzy Logic: Definition, Examples, and History MEDIUM
Fuzzy logic processes variables with multiple truth values, accommodating imprecise data for more accurate conclusions in various applications like AI and control systems.

Concepts and Tools

Learn about important deep learning concepts that will help you throughout the learning process, as well as tools that will help you build models and improve algorithms, such as Tensorflow, KERAS, and PyTorch.
Binary Classification
In this blog we have discussed Binary Classification, followed by developing, visualizing the Model.
Vectorization
This blog will study vectorization and use it in Machine Learning to write cleaner and optimized code.
Residual Analysis MEDIUM
In this blog, we will learn about Residual Analysis. We will further in the blog, learn about residual analysis in Python.
Vectorizing Logistic Regression
In this article, we will discuss the vectorized form of logistic regression. Furthermore, we will implement code for the vectorized form of logistic regression.
Understanding Forward Propagation
This blog thoroughly discusses Forward propagation in ANNs and how it differs from Backpropagation.
TensorFlow vs. PyTorch vs. Keras
In this blog, we will see three deep learning frameworks: Keras, TensorFlow, and PyTorch and their use cases. Let's begin.

Neural Networks

Neural networks are the foundation of deep learning. A network for prediction and accuracy is built by combining various nodes that are arranged in a meaningful structure. Artificial Neural Networks are another name for them. Learn about its different types, applications, and hands-on experience.
Artificial Neural Networks
This blog provides a high-level view of ANNs, particularly their architecture and their uses in the real world.
Bayesian Network in AI EASY
In this blog, we will learn about bayesian network in ai. We will understand its core concepts, its usage, advantages and disadvantages, and much more for better understanding.
Graph Neural Networks MEDIUM
In this article on Graph Neural Networks (GNN), we will understand GNN's fundamentals, syntax, practical examples with code and output, etc.
Multilayer Perceptron
The objective of this blog is to understand what multilayer perceptrons are.
Loss Functions in Neural Networks
In this article, we will learn about the loss function in neural networks. We’ll see some importance and types of loss functions.
Quantization and Pruning
This blog post will explore two crucial techniques. - Quantization and Pruning. - that enables the development of efficient deep neural networks while maintaining accuracy.
Author Arya27
0 upvotes
Introduction to Hopfield Neural Network
This blog discussed an introduction to Hopfield neural network. We will also discuss its architecture, energy function, and training model.

Convolution Neural Network

Convolution Neural Network (CNN) is a sophisticated deep learning algorithm that works best with images. It classifies images based on their various features. Because it is a deep learning algorithm, it can learn and implement at various stages of training.
Understanding of Convolutional Neural Network
The objective of this blog is to understand the Convolution Neural Network.
Padding in Convolutional Neural Network
This article discusses the padding in convolutional neural network in detail. It also discusses the need for padding and its use cases.
Author Alisha
0 upvotes
Convolution Layer
In this blog, we’ll see what convolution is and how to build a simple convolutional neural network to classify MNIST Digits.
Stride in Convolutional Neural Network (CNN) MEDIUM
Discover how stride impacts convolutional neural networks (CNNs). This article will cover topics like Stride in convolutional neural network, differences between stride and padding, and the effects of stride.
Pooling Layer in Convolutional Neural Network MEDIUM
This article discusses the Pooling Layer in Convolutional Neural Networks.
Convolution layer, Padding, Stride, and Pooling in CNN
The objective of this blog is to understand various layers of Convolution Neural Networks.
GPUs and CNN MEDIUM
This article covers the concept of usage of GPUs(Graphics Processing Units) in CNN(Convolutional Neural Networks).
Convolutional Neural Network in Keras
In this blog, we will see the implementation of Convolutional neural networks using Keras.
Data Augmentation
In this blog, we discussed data augmentation, sample model, horizontal and vertical augmentation followed by brightness augmentation.
Softmax and Cross-Entropy EASY
This article will study softmax function and cross-entropy with their implementation. Further, we will see why cross-entropy is used with the softmax function.
VGG-16 - CNN Model EASY
This blog will provide you with an overview of the VGG-16 and illustrate it using an object detection use-case.
AlexNet
In this article, we will learn about the architecture of the AlexNet with implementation. Important terminologies that are needed to build the AlexNet architecture.
Classic ConvNet Architectures HARD
This article discusses the different Classic ConvNet architectures with detailed explanations.
Author Alisha
2 upvotes
ZFNet EASY
This blog focuses on the approach and architecture of ZFNet
VGG Network EASY
VGG, a classic convolutional neural network (CNN) architecture discussed in this blog.
InceptionNet
This blog aims to explain what inceptionNet is and elucidate the evolution of its versions.
ResNet Architecture EASY
This article focuses on the concept, need, and implementation of ResNet
GoogLeNet Model
In this blog, we’ll learn the GoogLeNet model, its architecture, and its advantages over similar models.
Visualizing Convolutional Neural Networks with Filters
This blog explains the convolutional neural network. In detail, we will discuss how to enter visualizations in CNN, visualize convolutional layers, pre-fit the VGG model, how to visualize filters, and many more.
Author Aditi
0 upvotes
Guided Backpropagation
In this article, we will discuss guided backpropagation, use of guided backpropagation along with an example and a code implementation of it.
Fooling Convolutional Neural Network
This blog will focus on the basics behind the fooling of Convolution neural networks. Let's begin.
Dense In Deep Learning EASY
This article will focus on the denser layer in neural networks, different hyperparameters of Keras dense layer, and finally, a basic implementation.

Regularization

Regularization is a Deep Learning method for reducing noise and complexity in any model in order to avoid further complications such as overfitting. It is an important technique that can be used to significantly improve the model's overall performance.
Bias variance tradeoff EASY
The blog discusses the bias-variance tradeoff in detail and how it can be dealt with.
Early Stopping In Deep Learning
This article is about early stopping in deep learning and how it can solve the problem of overfitting
Parameter Sharing and Tying
In this article, we will learn about parameter sharing and tying and there use. Further, we will see the issues faced by l1 and how GROWL overcomes those issues.
Noise Injection in Neural Networks EASY
In this article, we try to discuss the concept of noise injection in neural networks and how it will work in neural networks by adding noise to the network to reduce overfitting.
Ensemble Method
This blog will look up Ensemble learning technique and implementation from scratch.
Dropout - Regularization Method
In this article, we will learn a powerful regularization technique i.e., dropout. Also will discuss, its uses.
Greedy Layer-wise Pre-Training HARD
In this article, we will learn greedy layer-wise pre-training in deep learning neural networks. Also, we will implement code for it.
Activation Functions - Introduction
Activation functions are the most essential aspect of any neural network as they are functions that help train neural networks in accordance with the data given.
Neural Network Activation Functions EASY
This article aims to understand various activation functions.
Batch Normalization - Introduction
In this blog, you will learn about the batch normalization method used to accelerate the training of deep learning neural networks.
Batch Normalization - Implementation
This blog focuses on Batch normalization and its implementation.

Recurrent Neural Networks

Recurrent Neural Networks are another type of Deep Learning Neural Network (RNN). It has memory, which is a key feature of RNN. Discover its architecture, implementation, and applications.
Recurrent Neural Network - Intro
In this blog, we discussed working, advantages, disadvantages, applications of the recurrent neural network(RNN)
Understanding an RNN cell
The objective of this blog is to understand RNN cells.
Understanding Bidirectional RNN MEDIUM
This blog will discuss Bidirectional RNN with suitable code examples, some advantages and disadvantages along with some frequently asked questions.
Sequence Models
In this article, we are going to discuss sequence models. Also, we will discuss in detail Recurrent Neural Network(RNN) and Long Short-Term Memory(LSTM).
Unrolling Recurrent Neural Network
This blog will focus on the requirements of Recurrent Neural Networks over Artificial Neural Networks.
Backpropagation Through Time-RNN
This article will look into BPTT, its algorithm, limitations, and advantages over normal Backpropagation.
Truncated BPTT
In this article, we will discuss truncated backpropagation through time (BPTT). Also will learn different techniques that will help in learning Truncated BPTT.
Gated Recurrent Units (GRUs) EASY
This article talks about the architecture and working of GRUs.
Time Series Prediction with GRU
In this article, we will read about Time-series Prediction with GRU. We will compare it with LSTM and see an example.
Long Short Term Memory(LSTM) Cells
This article will study LSTM cells, their working, architecture, and some of their applications.
Solving the Vanishing Gradient Problem with LSTM
In this article, we will discuss how the vanishing gradient problem is solved using LSTM (long short-term memory). Also, will see why simple RNN’s face this problem.
Bidirectional LSTM
In this article, we will learn the functioning of bi-directional long short-term memory(LSTM). Also will understand how to use Bi-LSTM in keras.
Stock Price Prediction Using LSTM
This article will study stock price prediction using the LSTM model and implement the same.
Encoder-Decoder Models
This article study about the encoder-decoder model, its architecture, it's working, and some of the applications.

Generative Adversarial Networks (GANs)

GANs, or Generative Adversarial Networks, are a popular unsupervised learning algorithm that learns on its own from given data and predicts for new data. One of the most intriguing Deep Learning approaches. StyleGAN and CycleGAN are two examples of applications.
Generative Adversarial Network
This blog will focus on an exciting AI tool used in machine learning called the Generative Adversarial Network and its applications. Let's begin.
Building GAN - Implementation
In this article, we will see a brief introduction to GAN(Generative Adversarial Network). Also will implement a GAN model to support the theory.
Model Collapse in GANs
This blog will focus on the model collapse in Generative adversarial network and the ways to tackle it.
Human Face Generation using GAN
This blog explains the details of Human Face Generation using GAN along with its working, details of generator and discriminator, and Training the GAN Model.
Deep Convolutional Generative Adversarial Networks
This blog will focus on The Deep Convolutional Generative Adversarial network and the detailed implementation.
Style based GAN
This blog talks about style based GAN or styleGAN
Introduction to CycleGAN EASY
In this article, we are going to learn the basics of CycleGANs, its architecture, why we are going to use it, what its advantages are, and also go through its uses.
Introduction to Conditional GANs
In this blog, we will discuss various types of GANs in deep learning, Conditional GANs, flexibility, and Application of Conditional GANs.
Super Resolution GAN EASY
This article will showcase the concept of Super-Resolution GAN, how it works to increase the resolution of an image, what its architecture is, its usage, etc.
Auxiliary Classifier GAN EASY
In this article, we will learn about the concept of Auxiliary classifier Gan how it works and its code implementation.
A Quick Guide to Diffusion Model
In this article, we will learn about the diffusion model, its architecture, implementation, and uses.