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Deep Learning Tutorial: For Beginners And Advanced Learners

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Deep Learning Tutorial Python is ideal for professionals aspiring to learn the basics of Python and develop applications involving Deep Learning techniques such as convolutional neural nets, recurrent nets, backpropagation.

According to the latest market research report Deep Learning Market by Offering (Hardware, Software, and Services), Application (Image Recognition, Signal Recognition, Data Mining), End-User Industry (Security, Marketing, Healthcare, Fintech, Automotive, Law), and Geography-Global Forecast to 2023, the overall deep learning market is estimated to be valued at $3.18 Billion in 2018 and is expected to be worth $18.16 Billion by 2023, at a CAGR of 41.7% from 2018 to 2023.

Improved computing power, falling hardware cost, and the higher-than-ever adoption of cloud-based technology are some of the primary drivers of the deep learning market. This has led software providers to offer software development kits and Deep Learning tutorials for empowerment.

Deep Learning Tutorial

Deep Learning Tutorials for Empowerment

What Is Deep Learning?

Deep Learning is a specialized branch of Machine Learning that uses supervised, unsupervised, or semi-supervised learning to learn from data representations.

Machine Learning is an approach or subset of Artificial Intelligence that is based on the idea that machines can be given access to data along with the ability to learn from it.

Deep Learning takes Machine Learning to the next level. Deep Learning Tutorial helps you learn the concept of TensorFlow along with its functions, operations, and the execution pipeline.

You will also learn about artificial neural networks, convolutional neural networks, and recurrent neural networks.

The three founding fathers of Deep Learning: Geoff Hinton of Google, Yann LeCun of AI Research at Facebook, and Yoshua Bengio of the University of Montreal conducted intensive research in Deep Learning and were torchbearers for a breakthrough discovery in 2006.

The three pioneers were successful in training deep neural networks to learn from useful representations.

Why Is Deep Learning So Important?

The underlying principle of discovery is that “deep neural networks” can learn from representations of data in an “unsupervised” manner.

This feature can is very effective in domains where a collection of data points must be analyzed together to convey meaning or information.

Deep Learning is no longer a trend, it is one of the game-changers in technology space. Renowned companies like Google, Facebook, Microsoft, and Baidu are using Deep Learning techniques.

This has led more and more technology professionals to take Deep Learning as a field of study.

Deep Learning Tutorials

Connection Between Artificial Intelligence, Machine Learning & Deep Learning

Enrolling for a Deep Learning Tutorial teaches you the basic principles of Deep Learning, implement several feature learning/deep learning algorithms, and also learn how to apply/adapt these ideas to new problems.

Some Well-Known Sources For Deep Learning Tutorial

(i) Andrew NG

Andrew Ng’s coursera online course is a suggested Deep Learning tutorial for beginners. Enrolling for this online deep learning tutorial teaches you the core concepts of Logistic Regression, Artificial Neural Network, and Machine Learning (ML) Algorithms.

Students will also learn about the application of linear regression to housing price prediction, cost function, and they are introduced to the gradient descent method for learning.

The course also includes programming assignments designed to help you understand how to implement the learning algorithms in practice.

(ii) Udacity

Udacity’s Deep Learning Tutorial includes modules on Keras and TensorFlow, convolutional and recurrent networks, deep reinforcement learning, and GANs.

The Deep Learning tutorial for beginners is taught by industry stalwarts like Sebastian Thrun, Ian Goodfellow, and Andrew Trask. In addition, students have unlimited access to Experts-in-Residence from OpenAI, GoogleBrain, DeepMind, and more.

(iii) NVIDIA Deep Learning Institute (DLI)

The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing that teaches you to solve real-world problems.

Designed for developers, data scientists, and researchers, the online Deep Learning tutorial is available in two formats: online courses and online electives.

DLI online course teaches students to implement and deploy an end-to-end project in eight hours. The users have access to a fully configured GPU-accelerated workstation in the cloud.

DLI electives, on the other hand, includes hands-on training on how to apply a specific technology or development technique in two hours. Like full-length courses, the electives can be taken anytime, anywhere, with access to GPUs in the cloud.

Deep Learning Tutorial

Deep Learning Tutorial

Deep Learning Tutorial Python

Deep Learning Tutorial Python is ideal for professionals aspiring to learn the basics of Python and develop applications involving Deep Learning techniques such as convolutional neural nets, recurrent nets, backpropagation.

If you enroll for a Deep Learning tutorial python you will be introduced to Python and its libraries like NumPy, SciPy, Pandas, Matplotlib; frameworks like Theano, TensorFlow, and Keras.

When enrolling for a Deep Learning Tutorial with Python make sure it includes the different libraries and frameworks that can be applied to solve complex real-world problems.

In our discussion on Deep Learning Tutorial, we will explore the aforementioned concepts and also how Deep Learning tutorial for beginners helps them understand data and programming with real-life examples and it to use various mathematical and statistical models. We would also allude to reference and reading materials like Deep Learning Tutorial pdf.

(i) Keras Deep Learning tutorial Python will teach you the basics of basics of Python deep learning and learn about Artificial Neural Networks.

With a step-by-step guide, the online deep learning tutorial teaches you how to use Python and its libraries to understand, explore and visualize your data, build up multi-layer perceptrons for classification tasks, how to use your model to predict target values, and how to validate the models.

Tutorials Point Deep Learning Tutorial Python will help you learn the basics of Python and develop applications involving Deep Learning techniques such as convolutional neural nets, recurrent nets, and backpropagation.

You will also get to know about the Python libraries like NumPy, SciPy, Pandas, Matplotlib and frameworks like Theano, TensorFlow, Keras. The tutorial explains how the different libraries and how these can be applied to solve complex real-world problems

(ii) Simplilearn’s Deep Learning with TensorFlow course helps you learn about deep learning concepts and the TensorFlow open source framework, implement deep learning algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for an exciting career in deep learning.

Deep Learning Tutorial: Books To Read

Yoshua Bengio’s book Deep Learning is strongly recommended for a deep learning tutorial. It contains a nice intro to Deep Learning and some useful material on the basics of machine learning as well.

The Deep Learning textbook is aimed at helping students and practitioners enter the field of Machine Learning in general and Deep Learning in particular. The online version of the book is available for free.

You will learn about Applied Math and Machine Learning Basics, Modern Practical Deep Networks, and Deep Learning Research on Linear Factor Models, Representation Learning, Structured Probabilistic Models for Deep Learning, Deep Generative Models, and many more.

Deep Learning Tutorial

Deep Learning Tutorial: Books to Read

An older, but the classic book is Chris Bishop’s book Neural Networks for Pattern Recognition provides a good intro to Deep Learning theory. The book gives you a comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition.

After a formal introduction of the basic concepts of pattern recognition, Neural Networks for Pattern Recognition describes techniques for modelling probability density functions and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. The book also important discussion on data processing, feature extraction, and prior knowledge.

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Deep Learning Tutorial: Online Resources

In addition, you may look up for online resources on Deep Learning Tutorial that offers interesting insights into Deep Architectures for AI, Deep Learning for NLP, Neural Networks, and Deep Hierarchies of Representations.

Reference matter in the form of Deep Learning Tutorial pdf, survey papers, Deep Learning code helps are helpful for understanding the basic concepts and algorithms used for unsupervised feature learning and Deep Learning.

Other online resources on Deep Learning include Michael Nielsen’s “Neural Networks and Deep Learning” online book: Neural networks and deep learning, and Geoff Hinton’s Neural Net lectures on course, Neural Networks for Machine Learning.

I would personally recommend Stanford’s course notes of CS231n: Convolutional Neural Networks for Visual Recognition (CS231n: Convolutional Neural Networks for Visual Recognition) and Stanford course (notes and videos): CS224d: Deep Learning for Natural Language Processing (CS224d: Deep Learning for Natural Language Processing) if you have a keen interest in deep learning for computer vision.

To know more about Deep Learning Frameworks read Torch’s Scientific computing for LuaJIT, TensorFlow, Welcome – Theano 0.7 documentation, and of course, Caffe’s notes on Deep Learning Framework.

Blogs and tutorials like Colah’s blog, WildML, and MLCube are very useful students of Deep Learning.

A Career in Deep Learning

Are you interested in a career in Deep Learning? Does researching about Deep Learning Python or Deep Learning with TensorFlow interest you? Do you want to make a career in deep learning, as a developer, programmer, scientist or researcher?

The exponential rise of data has led to an unprecedented demand for Big Data scientists and Big Data analysts. Enterprises must hire Data Science professionals with a strong knowledge of Deep Learning and Big Data applications.

Deep Learning Tutorial

Career in Deep Learning

The average salary for Deep Learning professionals ranges from approximately $77,562 per year for Research Scientist to $135,255 per year for Machine Learning Engineer.

Data Scientist, Analytics Manager, Database Administrator, Data Engineer, Data Analyst, and Business Intelligence Developer are some of the top 50 jobs listed in Glassdoor.

Deep Learning Tutorial

Glassdoor 50 Best Jobs in America, 2018

However, there is a sharp shortage of data scientists in comparison to the massive amount of data being produced. This makes hiring difficult and more expensive than usual.

Wrapping Up

Take up a Data Analytics or Data Science course, to learn Data Science skills and prepare yourself for the Data Scientist job, you have been dreaming of.

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In addition, students also get lifetime access to online course matter, 24×7 faculty support, expert advice from industry stalwarts, and assured placement support that prepares them better for the vastly expanding Big Data market.

A self-starter technical communicator, capable of working in an entrepreneurial environment producing all kinds of technical content including system manuals, product release notes, product user guides, tutorials, software installation guides, technical proposals, and white papers. Plus, an avid blogger and Social Media Marketing Enthusiast.

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