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Introduction to Deep Learning Online Training

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UPDATED: July 11, 2024
Beginner

This Deep Learning Training equips junior data scientists with the skills to implement deep learning solutions for image data, text classification, and time-series predictive modeling. Learn to use TensorFlow for computer vision (CV), natural language processing (NLP), and explore Large Language Models (LLMs). Ideal for onboarding new data scientists or as a data science reference resource.

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What you'll learn

  • Building and training neural networks for image and text classification
  • Learning the differences between machine learning and deep learning, and when to use one over the other
  • Understanding Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP) techniques
  • Evaluating and optimizing deep learning models using pre-trained models from TensorFlow, Google, and Kaggle

FAQs

Is it worth it to take an introductory deep learning course?

Yes, for particular people – especially anyone interested in a career in fields like data science or large data model engineering – learning about deep learning's fundamentals is definitely worth it. Of course, a tutorial on the basics of deep learning isn't necessary for anyone and everyone, but if data science, artificial intelligence research, or machine learning engineering is interesting to you, this course can help you get your first steps in the field and decide what's next.

What's the best way for a beginner to learn about deep learning?

A course like this is the best way to get introduced to the topic because of its emphasis on a strong foundation. This video-based course emphasizes familiarity with the broad concepts and essential vocabulary of the entire deep learning subject matter without endorsing a particular approach, technology suite or vendor. When you're just getting started, a short, agnostic course can help you choose your own path through the subject.

Does deep learning and machine learning require a lot of math?

Yes – even though this particular course on the basics of deep learning doesn't require more than familiarity with basic mathematical knowledge – deep learning takes a thorough understanding of fairly advanced math. Once you move beyond introductions to the topic, deep learning implementation depends on a grasp of linear algebra and calculus to start. As you move into deep learning approaches in building probability models and implementing optimization techniques, advanced math is even more important.

Can't I learn deep learning on my own?

Of course you can learn deep learning on your own. But it's worth thinking about what exactly it means to learn something on your own. You can learn a foreign language on your own, but you can't just make up the grammar and vocabulary. Artificial intelligence design and deep learning engineering are similar: they're highly complex topics with complicated implementations. Courses like this are the best introduction to deep learning's 'grammar' – after that, you can practice and improve on your own.

Are there any certifications associated with this course on deep learning?

No, this course isn't designed to help pass any particular certifying exam. Some hardware and software vendors have added topics about incorporating deep learning and AI/ML into their technology ecosystems to their certifications. But so far there's no IT certification that's specifically about the topic of deep learning writ large. This course teaches you the basic concepts and vocabulary of deep learning with no time spent on memorization or exam prep.

Who is this for?

This deep learning training is considered associate-level data science training, which means it was designed for data scientists who have a basic understanding of Python programming and machine learning concepts, including supervised and unsupervised learning.

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Study plan

Download the free Introduction to Deep Learning Online Training study plan to complete this course in about 34 hours.

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