Learn TensorFlow and deep learning, without a Ph.D.

Overview

Embark on a 3-hour journey with this course, offering developers an expedient introduction into the world of deep-learning fundamentals, supplemented with TensorFlow insights.

The realm of deep learning, synonymous with neural networks, is rapidly becoming a beacon for developers eager to harness the power of machine learning models. If the constraints of time have deterred your deep learning endeavors, you're not alone.

Reflecting on my academic years, I recall a math instructor who insisted that integrals were elementary, reminiscent of my encounters today with the complex world of deep learning online.

The foundational elements of deep learning, including concepts like “dropout," “cross-entropy," and neural network variants, often presume a level of familiarity that many potential learners, myself included, find elusive.

To demystify deep learning for developers hesitant to embark on a lengthy academic pursuit, I've designed this concise, 3-hour crash course. Tailored to simplify the learning process, the course covers essential network architectures such as dense, convolutional, and recurrent networks, alongside training methodologies like dropout and batch normalization.

Originally debuted at the Devoxx conference in Antwerp, Belgium, in November 2016, this course equips you with the skills to tackle common neural network problems and familiarizes you with the terminology and concepts necessary to continue your learning in deep learning, particularly through TensorFlow resources. (TensorFlow, Google's proprietary deep learning framework, has gained significant traction since becoming open-source in 2015.)

Categories:

Artificial Intelligence Courses, Deep Learning Courses, TensorFlow Courses

Provider:

Independent

Subjects