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IBM

Deep Learning with IBM

IBM via edX Professional Certificate

Overview

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.

Syllabus

Courses under this program:
Course 1: 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 thepopular Keras library.



Course 2: PyTorch Basics for Machine Learning

This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.



Course 3: Deep Learning with Python and PyTorch

This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.



Course 4: Deep Learning with Tensorflow

Much of theworld'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.



Course 5: Using GPUs to Scale and Speed-up Deep Learning

Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.



Course 6: Applied Deep Learning Capstone Project

In this capstone project, you'll use either Keras or PyTorch to develop, train, and test a Deep Learning model. Load and preprocess data for a real problem, build the model and then validate it.



Courses

Taught by

Joseph Santarcangelo, Alex Aklson and Saeed Aghabozorgi

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