Deep Learning Tutorial - GeeksforGeeks (2024)

Deep Belief Network (DBN) in Deep Learning

Discover data creation with Deep Belief Networks (DBNs), cutting-edge generative models that make use of deep architecture. This article walks you through the concepts of DBNs, how they work, and how to implement them using practical coding. What is a Deep Belief Network?Deep Belief Networks (DBNs) are sophisticated artificial neural networks used

Deep Boltzmann Machines (DBMs) in Deep Learning

In this article, we will discuss the Deep Boltzmann Machines concepts and their applications in the real-world scenario. What are Deep Boltzmann Machines (DBMs)?Deep Boltzmann Machines (DBMs) are a kind of artificial neural network that belongs to the family of generative models. They are designed to discover intricate structures within large datas

Unveiling the Power of Fastai: A Deep Dive into the Versatile Deep Learning Library

Fastai is a powerful deep-learning library designed for researchers and practitioners. It offers high-level abstractions, PyTorch integration, and application-specific APIs, making it both adaptable and accessible for a wide range of deep learning tasks. In this article, we'll delve into the intricacies of Fastai, a powerful deep-learning library.

Introduction to Multi-Task Learning(MTL) for Deep Learning

Multi-Task Learning (MTL) is a type of machine learning technique where a model is trained to perform multiple tasks simultaneously. In deep learning, MTL refers to training a neural network to perform multiple tasks by sharing some of the network's layers and parameters across tasks. In MTL, the goal is to improve the generalization performance of

Artificial intelligence vs Machine Learning vs Deep Learning

Nowadays many misconceptions are there related to the words machine learning, deep learning, and artificial intelligence (AI), most people think all these things are the same whenever they hear the word AI, they directly relate that word to machine learning or vice versa, well yes, these things are related to each other but not the same. Let's see

Need of Data Structures and Algorithms for Deep Learning and Machine Learning

Deep Learning is a field that is heavily based on Mathematics and you need to have a good understanding of Data Structures and Algorithms to solve the mathematical problems optimally. Data Structures and Algorithms can be used to determine how a problem is represented internally or how the actual storage pattern works & what is happening under

Difference Between Artificial Intelligence vs Machine Learning vs Deep Learning

Artificial Intelligence is basically the mechanism to incorporate human intelligence into machines through a set of rules(algorithm). AI is a combination of two words: "Artificial" meaning something made by humans or non-natural things and "Intelligence" meaning the ability to understand or think accordingly. Another definition could be that "AI is

ML | Natural Language Processing using Deep Learning

Machine Comprehension is a very interesting but challenging task in both Natural Language Processing (NLP) and artificial intelligence (AI) research. There are several approaches to natural language processing tasks. With recent breakthroughs in deep learning algorithms, hardware, and user-friendly APIs like TensorFlow, some tasks have become feasi

Deep Learning with PyTorch | An Introduction

PyTorch in a lot of ways behaves like the arrays we love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tensors and makes it simple to move them to GPUs for the faster processing needed when training neural networks. It also provides a module that automatically calculates gradients (for backpropagation) and another

Prediction of Wine type using Deep Learning

We use deep learning for the large data sets but to understand the concept of deep learning, we use the small data set of wine quality. You can find the wine quality data set from the UCI Machine Learning Repository which is available for free. The aim of this article is to get started with the libraries of deep learning such as Keras, etc and to b

Avengers Endgame and Deep learning | Image Caption Generation using the Avengers EndGames Characters

Behold, Marvel Fans. Avengers are out there to save the Multiverse, so are we, ready to do whatever it takes to support them. In this article, we will use Deep Learning and computer vision for the caption generation of Avengers Endgame characters. We will start will the basics, explaining concepts and use a pre-trained model to implement the projec

Deep Q-Learning

Q-Learning is required as a pre-requisite as it is a process of Q-Learning creates an exact matrix for the working agent which it can "refer to" to maximize its reward in the long run. Although this approach is not wrong in itself, this is only practical for very small environments and quickly loses it's feasibility when the number of states and ac

Implementing Deep Q-Learning using Tensorflow

Prerequisites: Deep Q-Learning This article will demonstrate how to do reinforcement learning on a larger environment than previously demonstrated. We will be implementing Deep Q-Learning technique using Tensorflow. Note: A graphics rendering library is required for the following demonstration. For Windows operating system, PyOpenGl is suggested wh

Differential Privacy and Deep Learning

Differential privacy is a new topic in the field of deep learning. It is about ensuring that when our neural networks are learning from sensitive data, they're only learning what they're supposed to learn from the data. Differential privacy is a concept in privacy-preserving data analysis that aims to protect the privacy of individuals while still

Human Activity Recognition - Using Deep Learning Model

Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same sequence, we can use LSTM (long short term memory) mode

Residual Networks (ResNet) - Deep Learning

After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common problem in deep learning associated with that called the

ML - Saving a Deep Learning model in Keras

Training a neural network/deep learning model usually takes a lot of time, particularly if the hardware capacity of the system doesn't match up to the requirement. Once the training is done, we save the model to a file. To reuse the model at a later point of time to make predictions, we load the saved model. Through Keras, models can be saved in th

DLSS - Deep Learning Super Sampling

Super Sampling is also known as Super Sampling Anti Aliasing(SSAA) is a spatial anti-aliasing method i.e. a method to remove aliasing (jagged and pixelated edges also known as "jaggies") from a video, rendered images or another software that produces computer graphics. Aliasing is not often dealt-with in higher resolutions but if the user does not

Introduction in deep learning with julia

A new transition in Data Science is Julia since it is fast and easy to learn and work with. Julia being a promising language is mainly focused on the scientific computing domain. It provides good execution speed which is comparable to C/C++. It also supports parallelism. Julia is good for writing codes in Deep Learning because deep learning framewo

Deep Learning in R Programming

Deep Learning is a type of Artificial Intelligence or AI function that tries to imitate or mimic the working principle of a human brain for data processing and pattern creation for decision-making purposes. It is a subset of ML or machine learning in an AI that owns or have networks that are capable of unsupervised learning from data that are unlab

5 Deep Learning Project Ideas for Beginners

Well, irrespective of our age or domain or background knowledge some things succeed in fascinating us in a way such that we're so motivated to do something related to it. Artificial Intelligence is one such thing that needs nothing more than just a definition to attract anyone and everyone. To be precise, Artificial Intelligence is the godfather. I

PointNet - Deep Learning

PointNet was proposed by a researcher at Stanford University in 2016. The motivation behind this paper is to classify and segment 3D representation of images. They use a data structure called Point cloud, which is a set of the point that represents a 3D shape or an object. Due to its irregularities, it is only suitable for a particular use case. Ma

Transformer Neural Network In Deep Learning - Overview

In this article, we are going to learn about Transformers. We'll start by having an overview of Deep Learning and its implementation. Moving ahead, we shall see how Sequential Data can be processed using Deep Learning and the improvement that we have seen in the models over the years. Deep Learning So now what exactly is Deep Learning? But before w

Top 10 Must-Read Books on Deep Learning

Deep Learning is a part of machine learning and artificial intelligence that uses multiple layers to extract useful information from raw data. From self-driven cars to voice assistant robots, all of these work on deep learning algorithms. As per the survey, it is observed that the global deep-learning market is expected to rise by USD 415.4 Billion

Difference between a Neural Network and a Deep Learning System

Since their inception in the late 1950s, Artificial Intelligence and Machine Learning have come a long way. These technologies have gotten quite complex and advanced in recent years. While technological advancements in the Data Science domain are commendable, they have resulted in a flood of terminologies that are beyond the understanding of the av

Deep Learning with Python OpenCV

Opencv 3.3 brought with a very improved and efficient (dnn) module which makes it very for you to use deep learning with OpenCV. You still cannot train models in OpenCV, and they probably don't have any intention of doing anything like that, but now you can very easily use image processing and use the pre-trained models to make predictions using th

Black and white image colorization with OpenCV and Deep Learning

In this article, we'll create a program to convert a black & white image i.e grayscale image to a colour image. We're going to use the Caffe colourization model for this program. And you should be familiar with basic OpenCV functions and uses like reading an image or how to load a pre-trained model using dnn module etc. Now let's discuss the pr

Age Detection using Deep Learning in OpenCV

In this tutorial, we are going to see the entire process of creating an Age predictor and Gender classifier project using OpenCV. Age Detection Our goal here is to create a program that will predict the gender and age of the person using an image. But predicting age might not be as simple as you think, why? You might be thinking that age prediction

Early Stopping for Regularisation in Deep Learning

When training big models with enough representational capacity to overfit the task, we frequently notice that training error drops consistently over time, while validation set error rises again. Figure 1 shows an example of this type of behavior. This pattern is fairly consistent. This means that by returning to the parameter setting at the moment