Deep Learning - Rxharun

Deep Learning

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.

Deep Learning is a subset of machine learning where artificial neural networks are inspired by the human brain. These further analyze and cumulate insights from that data, and later learn from the same. Any deep learning algorithm would reiterate and perform a task repeatedly, tweaking, and improving a bit every time, in order to improve the outcome. The term ‘deep learning’ is coined because neural networks have various layers that enable learning, unlearning, and relearning. Our Deep Learning tutorial will help you learn everything; what it is, what is a neural network, all the frameworks, and even prepare you for deep learning interviews. The deep learning tutorial also covers various skills and algorithms from CNN to RNN. So watch the deep learning tutorial to master the concepts and models using the TensorFlow framework and implement deep learning algorithms, and prepare yourself for a thriving career in deep learning.

Why Learn Deep Learning Tutorials?

Deep Learning is a subdivision of machine learning that imitates the working of a human brain with the help of artificial neural networks. It is useful in processing Big Data and can create important patterns that provide valuable insight into important decision-making. The manual labeling of unsupervised data is time-consuming and expensive. DeepLearning tutorials help to overcome this with the help of highly sophisticated algorithms that provide essential insights by analyzing and cumulating the data.

Deep Learning leverages the different layers of neural networks that enable learning, unlearning, and relearning. It covers all the essential skills and algorithms from CNN to RNN and thus provides smarter solutions – and so does the Deep Learning tutorial.

Applications of Deep Learning

Deep Learning, Artificial Intelligence, and Machine Learning are correlated with each other; they help to improve business processes and allow a business organization to stay ahead of the competition. The growth of Deep Learning has enabled organizations to offer smart and predictive solutions to customers. There is a variety of frameworks developed around Deep Learning to make it more accessible and feasible; it includes TensorFlow, Keras, PyTorch, Theano, DL4J, Caffe, and many more. These frameworks have increased the application of Deep Learning and allowed for easy integration of Machine Learning and AI functionality.

Deep Learning is often regarded as the most powerful technique of machine learning. It enables a smooth and synchronous connection between data science and AI.

Below mentioned are some of the technologies and services that use Deep Learning, Data Science, AI, and Machine Learning efficiently:

1. Speech Recognition

Digital assistants like Siri, Cortana, Alexa, and Google Now use deep learning for natural language processing and speech recognition. Deep Learning has made it possible to translate spoken conversations in real-time. Ask the digital assistant to locate nearby computer shops, and speech recognition software will work effortlessly without allowing you to type a single word.

Artificial neural networks have made it possible for computers and machines to interpret speech. Google Translate also uses deep learning and image recognition to translate voice and written languages.

2. Image Recognition

Deep learning-based image recognition is becoming the mainstream, as it produces more accurate results than humans. FDNA (Facial Dysmorphology Novel Analysis) is a deep learning-based technology that is used to analyze human malformation cases by understanding the patterns associated with genetic syndromes. Google also leverages deep learning at a large scale to deliver smart solutions. Google photos are a popular photo-sharing and storage service offered by Google that use the convolutional neural network to scan and tag the photos in the cloud and make them easily accessible.

3. Self-driving Cars

Deep Learning has gone into rigorous development over a decade and made it possible to transform traditional technologies. It has made possible autonomous car technology a reality. Deep Learning uses the complex layer of neural networks to analyze and interpret the data in real-time. It also helps in forecasting future events and provides valuable insights to tackle unforeseen problems in advance.

4. Social Networking Platform

Deep Learning, AI, and Machine Learning are integrated everywhere, from social media to music streaming services. The music streaming platform uses deep learning to understand and analyze the user behavior and suggests music that the listener might enjoy. Facebook also uses deep learning to identify the person in a picture and suggests relevant tags. It is slowly and gradually becoming an essential part of our life.

5. High-End Systems

The collaboration between deep learning, data science, and AI can develop high-end and cutting-edge machines. Computers that are powered by AI can collect, absorb, and process data much quicker than humans.

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We will study used cases of these applications in each section of this Deep learning tutorial.

Who Should Learn Deep Learning?

Deep Learning has enabled the handling of a large volume of structured and unstructured data in an efficient way. It uses a large amount of data and learns from it to solve complex problems. It helps in getting useful insights about Big Data by correlating the data and understands the pattern. Thus, it helps handle a large volume of structured and unstructured data. If this large amount of data is left unutilized, then it is of no use.

A beginner with a basic understanding of maths and programming language can start in the field of deep learning. However, intermediate and advanced level requires a deep understanding of ML literature, algorithms, and different frameworks like TensorFlow and PyTorch. It is important to have a sound knowledge of machine learning concepts such as linear and logistic regression. Deep learning tutorial is ideal for professionals like Software engineers, Data Scientists, Data Analysts, and Statisticians with interest in deep learning.

Prerequisites to Get the Best Out of Deep Learning Tutorial

It is the Deep Learning that is untapped and understaffed, while AI and machine learning has gained momentum in recent years. Candidates looking to pursue a career in the field of Deep Learning must have a clear understanding of the fundamentals of programming language like python, along with a good grip in statistics. It is important to have a clear and sound knowledge of basic machine learning. It is also required to have a good knowledge of statistics and probability, along with an understanding of artificial neural networks and artificial nodes that perform different operations.

Deep Learning and machine learning has made it possible to automate difficult tasks and thus have improved productivity and efficiency considerably.

Deep Learning Tutorial

1. What is Deep Learning, and How Does Deep Learning Work?

Deep Learning is often regarded as the cornerstone of the next revolution in the field of computing. It is a subdivision of machine learning that deals with creating patterns out of data by learning and improving with the help of sophisticated computer algorithms. It allows computers and machines to observe, learn, and react to complex situations faster than humans and is extensively used in image classification, language translation, and speech recognition.

Deep Learning is becoming mainstream, and it is important to understand how deep learning works and how it evolved from being nowhere to anywhere. Deep Learning tutorials help in understanding the core functionality of this cutting-edge technology.

2. What is Neural Network: Overview, Applications, and Advantages?

Artificial Neural Network is the main aspect of Deep Learning tutorial, a technology that powers several deep learning-based machines. It mimics the functioning of a human brain and provides useful data based on learning, relearning, and unlearning. It comes in handy for robotics and pattern recognition systems.

It has wide applicability in different domains, like Handwriting recognition, Stock-exchange prediction, Image compression, and can also solve sales professionals’ traveling issues. AI and machine learning are leading the advancements in the application of neural networks and can help in applying artificial intelligence and neural networks to solve real-world problems.

3. Neural Networks Tutorial

A neural network is a combination of advanced systems and hardware designed to operate and function like a human brain. It consists of different layers like an input layer, hidden layer, and output layer. It can perform tasks like a translation of texts, identification of faces, speech recognition, controlling robots, and a lot more.

It can perform different activation functions, like Sigmoid, Threshold function, ReLU function, and Hyperbolic Tangent function. Neural Network can be broadly categorized in Feed-forward Neural Network, Radial Basis Functions Neural Network, Kohonen Self-organizing Neural Network, Recurrent Neural Network, Convolution Neural Network, and Modular Neural Network. Neural Network helps in understanding the complexities of Deep learning tutorials and creates a clear pathway to excel at it.

4. Top 8 Deep Learning Frameworks

Business organizations are integrating machine learning and artificial intelligence into their existing system to draw useful insights and make important decisions. However, this integration requires a deep understanding of how machine learning and deep learning work and limits the feasibility. Such limitations can be removed with the help of deep learning frameworks.

Deep Learning frameworks allow business organizations to integrate machine learning and AI with little to no knowledge. Several frameworks can be easily used to make the most out of Deep Learning tutorials. It includes TensorFlow, Keras, PyTorch, Theano, DL4J, Caffe, Chainer, Microsoft CNTK, and many more. All of these deep learning frameworks come with their advantages, benefits, and uses. They also provide the functionality to shift back and forth between the most.

5. What is TensorFlow: Deep Learning Libraries and Program Elements Explained

TensorFlow is an open-source library developed by Google. It supports traditional machine learning and helps in building deep learning applications as well. It works in multi-dimensional arrays and can handle a large amount of data easily.

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It offers both C++ and Python APIs and also supports CPU and PU computing devices. TensorFlow works on two basic concepts, i.e., building a computational graph and executing a computational graph. TensorFlow makes it easier to store and manipulate the data using different programming elements like Constants, Variables, and Placeholders. TensorFlow has made the implementation of machine learning and deep learning models scalable and easier.

6. TensorFlow Tutorial For Beginners: Your Gateway to Building Machine Learning Models

AI is found everywhere, from self-driving cars to virtual assistants. While machine learning creates algorithms that allow machines to learn and apply intelligence, TensorFlow helps in building machine learning models inefficiently. TensorFlow makes code development easy and provides readily available APIs that help in saving time, making it more scalable.

Tensor, Tensor rank, and Tensor data type are the key elements of TensorFlow that helps in building and executing a computational graph. It supports different neural networks to create deep learning models.

7. Convolutional Neural Network Deep Learning Tutorial

A convolutional neural network is also known as ConvNet. It is a feed-forward neural network that is widely used to analyze visual images by processing data with grid-like topology. It can be used to detect and classify the objects in an image easily. A ConvNet has multiple layers like the Convolution layer, ReLU (rectified linear unit) layer, Pooling layer, and Fully connected layer that helps in the extraction of information from an image.

These different layers work in correlation to each other and provide valuable data sets to other layers. It has wide applicability and can be used in creating cutting-edge deep learning-based systems.

8. Recurrent Neural Network Tutorial

Neural Network is the most popular and widely used machine learning algorithm that is far superior to any other algorithms. There are different types of neural networks like Feed-forwarded, Convolutional, Deep belief, and Recurrent neural networks. Each of these has its limitations and advantages, and Recurrent neural networks were developed to overcome the limitations of the feed-forward neural network.

A recurrent neural network can be used for speech recognition, image captioning, voice recognition, time series prediction, and natural language processing. It works on the principle of saving the output of a particular layer and feeding the same to the input to predict the output of the layer. It can be of one to one, one to many, many to one, and many to many. It also helps in modeling time-dependent and sequential data problems.

9. Top Deep Learning Interview Questions and Answers

Deep Learning takes advantage of Big Data and helps in the structuring of data using complex algorithms to train neural networks. Neural networks replicate the working of the human brain and consist of three network layers, including the input layer, hidden layer, and output layer. It enables the business organization to tackle unforeseen changes and make predictive analyses to deliver a smarter solution.

Deep Learning frameworks allow us to integrate and implement machine learning and AI on a large scale with ease.

Deep learning vs. machine learning

If deep learning is a subset of machine learning, how do they differ? Deep learning distinguishes itself from classical machine learning by the type of data that it works with and the methods in which it learns.

Machine learning algorithms leverage structured, labeled data to make predictions—meaning that specific features are defined from the input data for the model and organized into tables. This doesn’t necessarily mean that it doesn’t use unstructured data; it just means that if it does, it generally goes through some pre-processing to organize it into a structured format.

Deep learning eliminates some of data pre-processing that is typically involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and it automates feature extraction, removing some of the dependency on human experts. For example, let’s say that we had a set of photos of different pets, and we wanted to categorize by “cat”, “dog”, “hamster”, et cetera. Deep learning algorithms can determine which features (e.g. ears) are most important to distinguish each animal from another. In machine learning, this hierarchy of features is established manually by a human expert.

Then, through the processes of gradient descent and backpropagation, the deep learning algorithm adjusts and fits itself for accuracy, allowing it to make predictions about a new photo of an animal with increased precision.

Machine learning and deep learning models are capable of different types of learning as well, which are usually categorized as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning utilizes labeled datasets to categorize or make predictions; this requires some kind of human intervention to label input data correctly. In contrast, unsupervised learning doesn’t require labeled datasets, and instead, it detects patterns in the data, clustering them by any distinguishing characteristics. Reinforcement learning is a process in which a model learns to become more accurate for performing an action in an environment based on feedback in order to maximize the reward.

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How deep learning works

Deep learning neural networks, or artificial neural networks, attempts to mimic the human brain through a combination of data inputs, weights, and bias. These elements work together to accurately recognize, classify, and describe objects within the data.

Deep neural networks consist of multiple layers of interconnected nodes, each building upon the previous layer to refine and optimize the prediction or categorization. This progression of computations through the network is called forward propagation. The input and output layers of a deep neural network are called visible layers. The input layer is where the deep learning model ingests the data for processing, and the output layer is where the final prediction or classification is made.

Another process called backpropagation uses algorithms, like gradient descent, to calculate errors in predictions and then adjusts the weights and biases of the function by moving backwards through the layers in an effort to train the model. Together, forward propagation and backpropagation allow a neural network to make predictions and correct for any errors accordingly. Over time, the algorithm becomes gradually more accurate.

The above describes the simplest type of deep neural network in the simplest terms. However, deep learning algorithms are incredibly complex, and there are different types of neural networks to address specific problems or datasets. For example,

  • Convolutional neural networks (CNNs), used primarily in computer vision and image classification applications, can detect features and patterns within an image, enabling tasks, like object detection or recognition. In 2015, a CNN bested a human in an object recognition challenge for the first time.
  • Recurrent neural network (RNNs) are typically used in natural language and speech recognition applications as it leverages sequential or times series data.

How to Create and Train Deep Learning Models

The three most common ways people use deep learning to perform object classification are:

Training from Scratch

To train a deep network from scratch, you gather a very large labeled data set and design a network architecture that will learn the features and model. This is good for new applications, or applications that will have a large number of output categories. This is a less common approach because with the large amount of data and rate of learning, these networks typically take days or weeks to train.

Transfer Learning

Most deep learning applications use the transfer learning approach, a process that involves fine-tuning a pretrained model. You start with an existing network, such as AlexNet or GoogLeNet, and feed in new data containing previously unknown classes. After making some tweaks to the network, you can now perform a new task, such as categorizing only dogs or cats instead of 1000 different objects. This also has the advantage of needing much less data (processing thousands of images, rather than millions), so computation time drops to minutes or hours.

Transfer learning requires an interface to the internals of the pre-existing network, so it can be surgically modified and enhanced for the new task. It has tools and functions designed to help you do transfer learning.

Feature Extraction

A slightly less common, more specialized approach to deep learning is to use the network as a feature extractor. Since all the layers are tasked with learning certain features from images, we can pull these features out of the network at any time during the training process. These features can then be used as input to a machine learning model such as support vector machines (SVM).

Accelerating Deep Learning Models with GPUs

Training a deep learning model can take a long time, from days to weeks. Using GPU acceleration can speed up the process significantly. Using MATLAB with a GPU reduces the time required to train a network and can cut the training time for an image classification problem from days down to hours. In training deep learning models, MATLAB uses GPUs (when available) without requiring you to understand how to program GPUs explicitly.

Get Started With Deep Learning Tutorial Now!

Deep Learning is an emerging field based on the principles of learning and improving with the help of sophisticated computer algorithms. Machine learning, Deep learning, and AI are all interrelated with each other. While machine learning uses simpler concepts of computing and data science, deep learning works with artificial neural networks.

Deep learning is gradually becoming the mainstream with the advent of AI and machine learning. It provides a great career prospect for those who are interested in statistics and data science. There has never been a better time to master this Deep learning tutorial that has the potential to become the future of computing.

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