Enroll for Free Online Deep Learning Courses on edX from top institutes like: IBM, Microsoft, The University of Queensland (UQ).
Top 5 Free Online Deep Learning Courses on edX

Deep Learning is an aspect of artificial intelligence that depends on data representations rather than task-specific algorithms. It allows the user to run supervised, semi-supervised, and unsupervised learning.

Deep Learning is inspired by the ways humans process information and then communicate through our own biological neural networks. These learning algorithms are able to process vast amounts of data, taking those datasets and building essential meaning. It's an expansion of machine learning, allowing the user to develop broader, more in-depth solutions as opposed to highly task-specific ones.

1- Using GPUs to Scale and Speed-up Deep Learning: IBM

Training a complex deep learning model with a very large dataset can take hours, days and occasionally weeks to train. So, what is the solution? Accelerated hardware.

You can use accelerated hardware such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time.

But the problem is that your data might be sensitive and you may not feel comfortable uploading it on a public cloud, preferring to analyze it on-premise.  In this case, you need to use an in-house system with GPU support. One solution is to use IBM’s Power Systems with Nvidia GPU and PowerAI. The PowerAI platform supports popular machine learning libraries and dependencies including Tensorflow, Caffe, Torch, and Theano.

In this course, you'll understand what GPU-based accelerated hardware is and how it can benefit your deep learning scaling needs. You'll also deploy deep learning networks on GPU accelerated hardware for several problems, including the classification of images and videos.

What you'll learn

Explain what GPU is, how it can speed up the computation, and its advantages in comparison with CPUs.
Implement deep learning networks on GPUs.
Train and deploy deep learning networks for image and video classification as well as for object recognition.

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IBM is a cognitive solutions and cloud platform company headquartered in Armonk, NY. It is the largest technology and consulting employer in the world, serving clients in more than 170 countries. With 25 consecutive years of patent leadership, IBM Research is the world's largest corporate research organization with more than 3,000 researchers in 12 labs located across six continents.

2- Deep Learning with Tensorflow: IBM

Much of the world's data is unstructured. Think images, sound, and textual data. Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems.

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world.

TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

What you'll learn

Explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.
Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.
Understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

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3- Deep Learning Explained: Microsoft

Learn an intuitive approach to building the complex models that help machines solve real-world problems with human-like intelligence.

Deep learning is a key enabler of AI powered technologies being developed across the globe. In this deep learning course, you will learn an intuitive approach to building complex models that help machines solve real-world problems with human-like intelligence. The intuitive approaches will be translated into working code with practical problems and hands-on experience. You will learn how to build and derive insights from these models using Python Jupyter notebooks running on your local Windows or Linux machine, or on a virtual machine running on Azure. Alternatively, you can leverage the Microsoft Azure Notebooks platform for free.

This course provides the level of detail needed to enable engineers / data scientists / technology managers to develop an intuitive understanding of the key concepts behind this game changing technology. At the same time, you will learn simple yet powerful “motifs” that can be used with lego-like flexibility to build an end-to-end deep learning model. You will learn how to use the Microsoft Cognitive Toolkit — previously known as CNTK — to harness the intelligence within massive datasets through deep learning with uncompromised scaling, speed, and accuracy.

What you'll learn

The components of a deep neural network and how they work together
The basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for
A working knowledge of vocabulary, concepts, and algorithms used in deep learning

How to build:

An end-to-end model for recognizing hand-written digit images, using a multi-class Logistic Regression and MLP (Multi-Layered Perceptron)
A CNN (Convolution Neural Network) model for improved digit recognition
An RNN (Recurrent Neural Network) model to forecast time-series data
An LSTM (Long Short Term Memory) model to process sequential text data

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Microsoft experts, let you learn through hands-on experience with broad reach, cutting-edge technologies in areas including cloud services, mobile development, and data sciences. Whether you’re a student or a seasoned technologist, Microsoft experts can empower you to build innovative applications, services, and experiences on the Microsoft platform that will help you make a meaningful impact in today’s interconnected world.

4- Deep Learning with Python and PyTorch: IBM

Learn how to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorch Deep Learning library. You'll then apply them to build Neural Networks and Deep Learning models.

The course will teach you how to develop Deep Learning models using Pytorch while providing the necessary deep-learning background.

What you'll learn

Explain and apply knowledge of Deep Neural Networks and related machine learning methods;
Know how to use Python, and Python libraries such as Numpy and Pandas along with the PyTorch library for Deep Learning applications;
Build Deep Neural Networks using PyTorch.

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5- Deep Learning through Transformative Pedagogy: UQ

How can powerful teaching strategies and effective learning activities enhance deep learning?

This education course has been developed for educators and education leaders. It explores deep learning by bringing together the most up-to-date research from cognitive psychology, contemporary educational theories, and neuro-scientific perspectives.

Deep learning encourages students to become creative, connected, and collaborative problem solvers; to gain knowledge and skills for lifelong learning; and to use a range of contemporary digital technologies to enhance their learning.

To facilitate deep learning, teachers will learn how to employ a diverse range of powerful teaching strategies and authentic learning activities to assist students to become independent thinkers, innovative creators, and effective communicators. Throughout each module, suggested learning experiences are provided for school or system leaders who seek to engage with deep learning practices across their organisation. In this way, the course is differentiated to cater to both individual learners and to groups.

What you'll learn

The features of surface and deep learning
Neuroscientific, psychological, and educational theories supporting deep learning
The importance of communication skills in deep learning
The role of student motivation and positive social relationships in deep learning
How effective feedback can support deep learning
How the thoughtful use of technology can support education

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The University of Queensland (UQ), Australia, is one of the world’s premier teaching and research institutions. Striving for excellence through the creation, preservation, transfer and application of knowledge UQ ranks in the top 50 Universities as measured by the QS World University Rankings. UQ is one of only three Australian members of the global Universitas 21 and a founding member of the Group of Eight (Go8) universities. UQ is recognised for its world standard specialised research and teaching excellence; having won more Australian Awards for University Teaching than any other in the country.

Deep Learning In Business

Deep learning models are transforming the way we approach business. Humans produce a massive amount of data, but up to now, we've been unable to use it fully. Now, deep learning algorithms are providing learning techniques and real-world solutions based on these large data sets.

Business use cases include Netflix and Amazon recommendations based on single and multi-user behavior patterns and nontraditional cases such as using the algorithms to identify pests in agricultural practices.

Explore Careers in Deep Learning

Deep learning has the potential to change the way businesses make decisions now that we can take massive amounts of unstructured data and build programs that can analyze natural language, sentiment, and other complicated data the way the human brain would, only better. The kind of computing power you have with deep learning could revolutionize the ways businesses make decisions and design customer journeys. Providing those insights makes you highly valuable for some of the world's most cutting edge companies.