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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 the 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 TensorFlow framework and implement deep learning algorithms, and prepare yourself for a thriving career in deep learning.

- Image processing
- Video processing

The topics covered in this Deep Learning tutorial are:

- What is Deep Learning
- The Best Introduction to Deep Learning
- Deep Learning Applications
- What is Neural Network
- Neural Networks Tutorial
- Deep Learning Frameworks
- Deep Learning Algorithms
- Deep Learning With Python
- What is Tensorflow
- Install TensorFlow on Ubuntu
- What Is TensorFlow 2.0
- TensorFlow Tutorial for Beginners
- Convolutional Neural Network
- Recurrent Neural Network (RNN)
- What GANs Are
- What Is Keras
- Deep Learning Interview Questions

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.

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:

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.

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.

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.

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.

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.

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

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

Name | Date | Place | |
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Deep Learning with Keras and TensorFlow | 18 Dec -23 Jan 2022, Weekend batch | Your City | View Details |

Deep Learning with Keras and TensorFlow | 15 Jan -6 Feb 2022, Weekend batch | New York City | View Details |

Deep Learning with Keras and TensorFlow | 18 Feb -12 Mar 2022, Weekdays batch | San Francisco | View Details |

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