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Looking to master all things about deep learning and artificial neural networks? Wondering how to learn deep learning algorithms like an expert? Willing to better understand artificial neural networks, convolutional neural network and recurrent neural networks? This Deep Learning A-Z™: Hands-On Artificial Neural Networks course can help serve as your guide. This Deep Learning A-Z™: Hands-On Artificial Neural Networks course is one of the best-selling deep learning courses on Udemy, created in a collaboration with Kirill Eremenko, Hadelin de Ponteves and SuperDataScience team. This course is intended for providing people with a very practical and hands-on deep learning tutorial, helping them understand how to create deep learning algorithms and how to utilize the powerful tools of Tensorflow and Pytorch. At the end of this course, you will be expert in working with the cutting-edge deep learning technology.
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Introduction to The Deep Learning A-Z Course
As of 10/2019, there have been over 182,045 students enrolled and the Deep Learning A-Z™: Hands-On Artificial Neural Networks course has an average 4.5 rating. Most students are very satisfied with what they learnt. In this course, you’ll learn everything about deep learning in Python. You will learn how to be a data scientist, you’ll learn how to master deep learning as a professional analyst, you’ll learn how to level up your current level of deep learning, you will learn how to work with deep learning effortlessly, you will learn how to use the powerful tools for deep learning. Besides that, This Deep Learning A-Z™: Hands-On Artificial Neural Networks course will help you be capable of creating the cutting edge and powerful Deep Learning models and techniques.
To learn this Deep Learning A-Z™: Hands-On Artificial Neural Networks course requires learners know the basics of Python programming language. And the knowledge of high school mathematics is required.
This Deep Learning A-Z™: Hands-On Artificial Neural Networks course is designed for anyone who are interested in learning more about deep learning, no matter who you are, students, analysts, data scientists, entrepreneurs, business owners, or developers. This course will be a good start if you want to level up in deep learning and master the most popular deep learning algorithms.
What you will learn:
- What the intuition is
- How to use artificial neural networks practically
- What convolutional neural networks is
- How to apply convolutional neural networks
- What recurrent neural networks is
- How to apply recurrent neural networks
- What the self-organizing maps is and how to apply it
- What the Boltzmann machines is and how to apply it
- What the AutoEncoders is and how to apply it
More About Deep Learning
In this Deep Learning A-Z™: Hands-On Artificial Neural Networks course, you will learn all things about deep learning. What is deep learning? Artificial Intelligence, as we all know is the future. It evolves and grows its thinking capabilities by observing patterns in humans. It imitates how the human brain works, for data processing and pattern creation which eventually helps it in making effective decisions. This function of artificial intelligence is known as deep learning.
How does it work?
It draws all its learning from big data. Big data as we all know is a huge load of data that is not structured and which is taken from sources like the e-commerce websites, social media, search engines on the internet, online applications and a lot of similar places. This data can be accessed very easily, but not by the human mind. For a human mind, it may take many years to comprehend a small piece of such data.
How it competes with machine learning?
The most common technique used by the AI for processing of the big data is machine learning. It evolves with the addition of more and more data or as it gains more experience. Deep learning, on the other hand, is kind of a smaller refined version of machine learning. It makes use of artificial neural networks to do the work just like a human brain. The nodes of neurons are connected just like in a human brain. The process happens in multiple layers, where one later process one particular data and passes it to another layer.
An example of deep learning
Let’s consider a case of detecting fraudulent services or money laundering. A traditional approach will rely heavily on the number of transactions happening. On the other hand, a deep learning technique would take into account multiple other factors in play such as IP addresses, geographic locations, time, etc.
It is of major help to companies as it provides synthesized data about customers to them for developing user-friendly products and services according. Consumers also get more products customized according to their needs.
Convolutional Neural Networks
In the Deep Learning A-Z™: Hands-On Artificial Neural Networks course, you will understand what the convolutional neural network is. If we talk in layman’s terms then convolutional neural networks (CNN) are a kind of neural networks whose primary function is to do the classification of pictures or a cluster of pictures by taking into account the similarity that they have and do recognition of the objects. Convolutional neural networks are used for identification of data that is visual like faces of people, various types of animals, street signs, etc.
Parameter sharing and local connectivity
Parameter sharing and local connectivity are two very important concepts of Convolutional neural networks. The meaning of Parameter here is that the weight is shared by all of the neurons in one kind of a feature whereas local connectivity means that each neural is not connected to the input image completely but a subset of it. This doesn’t usually happen in the case of neural networks.
One of the parts or we can say building blocks of the convolutional neural networks is the pooling layer. The function of pooling layer is to reduce the spatial size of the representation in a progressive way so that the number of parameters and the computation in the network is slightly reduced. This operator is an independent way on each feature map. Max pooling is the most commonly used feature in pooling. To know more about the convolutional neural networks, the Deep Learning A-Z™: Hands-On Artificial Neural Networks course is highly recommended.
There is a nonlinearity in the CNN, which gets applied in a way similar to the neural networks and it’s called RELU. There is also a layer of neurons, which are joined fully at the end of CNN. The neurons of one layer have total connections to the neurons of previous layers.
CNN’s are a significant part of the computer vision. It has applications in the development of self-driven cars, robots, etc. It also has uses in developing OCR to digitize text.
Recurrent Neural Network
In the Deep Learning A-Z™: Hands-On Artificial Neural Networks course, you will know what the recurrent neural networks is. RNN can be elaborated as Recurrent Neural Network. This is a type of artificial neural network that connects or joins one neural node to others and forms a direct graph or temporal sequence, which helps it to show temporal dynamic behavior. In other words, in the recurrent neural network, the output that you get from the previous step becomes or acts as the input for the next step and this goes on until the final results are obtained.
The traditional neural networks were not like this, they did not use the previous output as the input for the next step. But a recurrent neural network works on this formula only.
What is a recurrent neural network used for?
The following are the uses of the recurrent neural networks.
They are used for:
- Captioning of image
- Stock market prediction
- Composition of music
- Prediction of the next word
- Recognition of speech
- Anomalous detection of time series
- Recognition of unsegmented or connected handwriting
What are the advantages of a recurrent neural network?
To learn the advantages of recurrent neural networks, the Deep Learning A-Z™: Hands-On Artificial Neural Networks course can help you. The benefits of the recurrent neural networks are as follows:
- It has a good memory, as it remembers all the information that makes it useful for series predictions.
- It can memorise the previous inputs, this is known as long short term memory.
- They are used in convolutional layers.
What are the disadvantages of a recurrent neural network?
The disadvantages of a recurrent neural network are as follows:
- The training of a recurrent neural network is a tough task to deal with.
- It is not good for long sequence processing.
- It has gradient disappearing and exploding issues.
With time science and technology have also evolved a lot. New technologies are coming up every day and they are helping the humankind in some way or the other. Some technologies are used in minor day to day work to make life easier and some technologies are of an advanced level that is used in deeper works, RNN is one of such network technology.
What is Machine Learning & Why It Matters?
The Deep Learning A-Z™: Hands-On Artificial Neural Networks course is taught by two top-rated machine learning and data science experts. Machine learning is the process of data analysis which is used for mechanizing the analytical model buildings. Machine learning is a branch of artificial intelligence that is based upon the idea that believes that systems can acquire knowledge from data, make decisions without or negligible human interference and can also identify patterns.
In other words, the capacity to acquire new data through iteration, without any form of dependency is called machine learning. Systems learn from the pre – computations or the earlier transactions and computations and then use pattern recognition to reach a valid and informed result.
What is the importance of machine learning?
There are various uses of machine learning, some of them are stated below:
- Machine learning allows timely analysis and arrangement of data in the system and so makes it easier to asses them which will help make good strategies to organise and manage your company or even business for that matter.
- Machine learning allows fast data processing and that too from multiple sources.
- Machine learning does not require human interference and so it saves a lot of time and makes work easier, like for example the antivirus is a kind of machine learning and it does not require human help to works, it protects our system and safeguards it from all the harmful viruses.
- Machine learning can identify spam in the system.
Many industries are getting transformed by the use of machine learning. The oil and gas industries are taking the help of advanced machine learning and through that, they predict system failure and also analyze the sources of energy and minerals in the ground. Many such things are done using machine learning these days, so it can be concluded that it is a big achievement in terms of technology for the world.
In the Deep Learning A-Z™: Hands-On Artificial Neural Networks course, you will clearly understand what the data preprocessing is. Data preprocessing is a method of data mining that includes the transformation of raw or crude data into a processed form of data that can be read and understood in general terms. The method of data preprocessing allows the data to go for further processing. In the real world, the data is generally inconsistent, incomplete and lack certain trends, specifications, and behaviors. They are most likely to contain errors and the method of data preprocessing helps to solve all these problems. Mostly the applications that are dependant on database use data preprocessing method to manage customer relationships and many such works.
What are the different steps of data preprocessing?
All the raw or crude data goes through several steps before they are pre-processed. The steps are mentioned below:
- The first step of data preprocessing is data cleansing where the data is cleansed and goes through several steps like smoothing the noisy data, resolution of inconsistencies in the data and auto-filling of the data that are missing.
- The 2nd step is data integration. All the data from various representations are assembled and the problems in them are resolved so that they can be sent for further processing.
- In the 3rd step the data is aggregated, normalized and generalized, in other words, data transformation takes place.
- In the 4th step the all the data that is present in the storage is reduced and represented. This step is all about data reduction.
- 5th step is about data separation.
Data preprocessing is a very important process in today’s world that is full of technologies and all the works have become digitalised. This Deep Learning A-Z™: Hands-On Artificial Neural Networks course will tell you how important it is. In this digital world where everything has become advanced, there is a dire need of data preprocessing that enables the crude data to be transformed into a readable form of information. Through this process, the data that we get in incomplete form, that lack certain attributes and behaviors are converted into a useful form of data or information that can be used by the systems.
Refer to more data & analytics courses here；
- Python for Data Science and Machine Learning Bootcamp
- Machine Learning A-Z™: Hands-On Python & R In Data Science
- The Data Science Course 2019: Complete Data Science Bootcamp
- R Programming A-Z™: R For Data Science With Real Exercises!
- Data Science A-Z™: Real-Life Data Science Exercises Included
If you want to break into deep learning, this Deep Learning A-Z™: Hands-On Artificial Neural Networks course will help you do so. This course is one of the best-selling and most highly sought-after online tutorials in data and analytics. It will help you become very good at creating deep learning algorithm in Python. Whether you are just stepping into deep learning or already have experience with the cutting-edge deep learning skills and techniques, this Deep Learning A-Z™: Hands-On Artificial Neural Networks course will teach everything you need to start a career in data and analytics or become a high-paid data scientist.
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