Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R
InstructorStart-Tech AcademyComidoc has tracked 1 coupon for this course since 2025, last checked 10 months, 26 days ago.
| Coupon code | Discount | Added | Status | Lifetime |
|---|---|---|---|---|
| SMARTLEARNFREE | 100% off | Expired | ~4 daysRan full term |
The curriculum provides separate tracks for building and training models in both Python and R environments.
Instructional signals indicate that theoretical concepts are explained thoroughly before demonstrating practical coding implementations.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide illustrates how neural networks create complex pattern recognition rules by processing training data through a network architecture to enable future predictions.
This course flow diagram outlines the curriculum progression from single-cell perceptrons through multi-level networks to implementation in Python.
This slide introduces the Fashion MNIST dataset used for classification tasks, showing examples of ten distinct fashion categories such as ankle boots, pullovers, and trousers.
This slide illustrates the challenge of handwriting recognition by displaying a grid of varied handwritten digits, emphasizing that creating precise rules for identification is difficult.
The content focuses specifically on Artificial Neural Networks, which may feel restrictive for those expecting a broader deep learning survey. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
One signal suggests that difficulty with instructor accents may necessitate the use of subtitles for comprehension. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
The curriculum aligns with the goal of teaching Python and R basics alongside neural networks for data science aspirants.
