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Romeo Kienzler, Alex Aklson, Joseph Santarcangelo, SAEED AGHABOZORGI, Yi Leng Yao, Sacchit Chadha, Nayef Abou Tayoun, Aije Egwaikhide, Samaya Madhavan, and Saeed Aghabozorgi

AI is revolutionizing the way we live, work and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware. The demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers -- and the job salaries of Deep Learning practitioners -- are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career.

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AI is revolutionizing the way we live, work and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware. The demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers -- and the job salaries of Deep Learning practitioners -- are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career.

Within this series of courses, you’ll be introduced to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. You’ll also master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision.

Throughout this program you will practice your Deep Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You’ll also complete the program by preparing a Deep Learning capstone project that will showcase your applied skills to prospective employers.

This program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning.

What you'll learn

  • Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
  • 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.
  • Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.

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What's inside

Six courses

Deep Learning Fundamentals with Keras

(15 hours)
Looking to kickstart a career in deep learning? This course will introduce you to the field and teach you the fundamentals. You will learn about the exciting applications of deep learning, the basics of neural networks, different deep learning models, and how to build your first deep learning model using the easy yet powerful library Keras.

Deep Learning with Python and PyTorch

(18 hours)
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch. In this course, you will learn how to build deep neural networks in PyTorch and train these models using state of the art methods.

Deep Learning with Tensorflow

(15 hours)
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.

Applied Deep Learning Capstone Project

(15 hours)
In this capstone project, you'll develop, train, and test a Deep Learning model using a library of your choice. Load and preprocess data for a real problem, build the model, and validate it. Finally, present a project report demonstrating your model's validity and your proficiency in deep learning.

PyTorch Basics for Machine Learning

(22 hours)
This course is the first part in a two part course and will teach you the fundamentals of Pytorch while providing the necessary prerequisites you need before you build deep learning models. We will start off with PyTorch's tensors in one dimension and two dimensions, you will learn the tensor types an operations, PyTorchs Automatic Differentiation package and integration with Pandas and Numpy.

Computer Vision and Image Processing Fundamentals

(10 hours)
Computer Vision is one of the most exciting fields in Machine Learning, computer science and AI. It has applications in many industries such as self-driving cars, robotics, augmented reality, face detection in law enforcement agencies. This intro-level course will teach you about computer vision and its various applications across many industries.

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