Learn Top Online Deep Learning Courses to have good hands-on Algorithms

Deep Learning Tensorflow Data Neural Networks Google Software Deep Architectures Convolutional Networks Machine Learning Microsoft Datasets Natural Language Processing Python

Learn Top Online Deep Learning Courses to have good hands-on Algorithms

By ridhigrg |Email | Sep 6, 2019 | 4317 Views

Deep Learning with Tensorflow

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.

About this course

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 constitutes 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.

This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.

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.

Deep Learning Explained

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

About this course

Machine learning uses computers to run predictive models that learn from existing data to forecast future behaviors, outcomes, and trends. Deep learning is a sub-field of machine learning, where models inspired by how our brain works are expressed mathematically, and the parameters defining the mathematical models, which can be in the order of few thousands to 100+ million, are learned automatically from the data.

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 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.

edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

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

Deep Learning Fundamentals with Keras

New to deep learning? Start with this course, that will not only introduce you to the field of deep learning but give you the opportunity to build your first deep learning model using the popular Keras library.

About this course

Looking to kickstart a career in deep learning? Look no further. This course will introduce you to the field of deep learning and teach you the fundamentals. You will learn about some of the exciting applications of deep learning, the basics fo neural networks, different deep learning models, and how to build your first deep learning model using the easy yet powerful library Keras.

What you'll learn

You will learn about exciting applications of deep learning and why it is really rewarding to learn how to leverage deep learning skills.
You will learn about neural networks and how they learn and update their weights and biases.
You will learn about the vanishing gradient problem.
You will learn about building a regression model using the Keras library.
You will learn about building a classification model using the Keras library.
You will learn about supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library.
You will learn about unsupervised learning models such as autoencoders.

Professional Certificate in Deep Learning

What you will learn

Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.
Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
Master Deep Learning at scale with accelerated hardware and GPUs.

Deep Learning with Python and PyTorch

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.

About this course

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

We'll start off with PyTorch's tensors and its Automatic Differentiation package. Then we'll cover different Deep Learning models in each section, beginning with fundamentals such as Linear Regression and logistic/softmax regression.

In the final part of the course, we'll focus on Convolutional Neural Networks and Transfer Learning (pre-trained models). Several other Deep Learning methods will also be covered.

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.

Source: HOB

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