Hands-On Deep Learning Deep Dive is a fast-paced, hands-on four day course that teaches students the modern skills and concepts that reside in the mathematical side of ML / DL, providing attendees with a solid platform for next-level, continued learning in this space based on role or goal.
Working in a hands-on learning environment led by our expert Machine Learning practitioner, students will explore the fundamentals of Machine Learning, Neural Networks, Deep Learning and Recurrent Neural Networks. Attendees will learn about various applications within this space. This course emphasizes mathematical machine learning algorithms and deep learning concepts. Hands-on labs leverage Python programming as the language of choice.
This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern machine learning experience into every classroom and hands-on project.
Students will learn about and work with:
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be easily adjusted to accommodate. We offer additional related Machine Learning, AI, Deep Learning, data science, programming (Python, R, Java, Scala etc.) and development courses which may be blended with this course for a track that best suits your learning objectives. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This in an intermediate-level course is geared for experienced developers or others (with prior Python experience) intending to start using learning about and working with machine learning algorithms, machine learning, deep learning fundamentals and concepts. . Attendees should be experienced developers who are comfortable with Python programming. Students should also be able to navigate Linux command line, and who have basic knowledge of Linux editors (such as VI / nano) for editing code.
Some of the related useful skills
Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.
Mathematical Concepts (Theoretical)
Set up/Test Drive with Python/Jupyter
Machine Learning
ML Decision Trees
Machine Learning Probability & Naïve Bayes Classifier
ML Linear Regression
ML Gradient Descent Algorithm
ML SVM Classifier
ML Summary
Deep Learning – AI Overview
Deep Learning – Overview & Basics (Theoretical)
Deep Neural Networks
GPU Programming Overview (Theoretical)
Student Materials: Each student will receive a Student Guide with course notes, code samples, setp-by-step written lab instructions, software tutorials, diagrams and related reference materials and links (as applicable). Students will also receive related (as applicable) project files, code files, data sets and solutions required for any hands-on work.
Lab Setup Made Simple. All course labs and solutions, data sets, software, detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.
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