Program profile · official sources
Deep Learning with TensorFlow 2
365 Data Science's 'Deep Learning with TensorFlow 2' is an advanced course taught by Iskren Vankov and Iliya Valchanov, designed to teach neural network foundations and practical implementation. Across a five-hour headline duration, the curriculum covers core machine learning mathematics, gradient descent, loss functions, activation mechanisms, backpropagation, and overfitting prevention via early stopping and regularization. Practical exercises walk students through translating linear models into TensorFlow 2, classifying handwritten digits from the MNIST dataset, and deploying deep nets for a customer conversion business case. Offering 11.5 CPE credits, credentialing requires passing the final exam with 60% or higher. Intermediate Python and machine learning skills are required.
Facts checked 27 Sep 2026
- Price
- See pricing
Price observation recorded 27 Sep 2026 (not a current price): No total course price is stated. The platform-wide Self-Study plan displays a regular comparison price of $36/month, an observed $12.50/month billed annually offer, and an FAQ mentions a $29/month rate, as observed in United States captures dated 2026-09-27. ISO currency and a separately monthly-billed amount were not established; these subscription rates are not treated as a source-checked completion total. Certificates are included with the Self-study learning plan.
- Time
- 5 hours
- (provider headline; module and exam timings listed separately)
- Level
- Advanced
- Credential
- Provider-issued certification
Verifiable credential with Credential ID and Credential Link; sharable on LinkedIn/resumes; downloadable upon passing. Certificates are included with the Self-study learning plan.
- Prerequisites
- Python (version 3.8 or later), TensorFlow 2 library, and a code editor or IDE (e.g., Jupyter Notebook, VS Code, or Google Colab) Intermediate Python and machine learning knowledge is required. Familiarity with NumPy and neural network fundamentals is recommended.
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
- Duration
- 5 hours (provider headline; module and exam timings listed separately)
- Level
- Advanced
- Format
- 100% online, self-paced course
- Credential
- Provider-issued certification
- Prerequisites
- Python (version 3.8 or later), TensorFlow 2 library, and a code editor or IDE (e.g., Jupyter Notebook, VS Code, or Google Colab) Intermediate Python and machine learning knowledge is required. Familiarity with NumPy and neural network fundamentals is recommended.
Sources ↗
No accepted numeric price in this registry. See the sourced pricing details below.
See the full registry price landscape. Prices retain their stated payment scope; they are not comparable completion costs.
What’s covered
- Introduction
- Neural networks Intro
- Setting up the environment
- Minimal example
- Introduction to TensorFlow 2
- Deep nets overview
- Backpropagation (optional)
- Overfitting
- Initialization
- Optimizers
- Preprocessing
- Deeper example
- Business case
- Conclusion
- Course exam
Prerequisites: Python (version 3.8 or later), TensorFlow 2 library, and a code editor or IDE (e.g., Jupyter Notebook, VS Code, or Google Colab) Intermediate Python and machine learning knowledge is required. Familiarity with NumPy and neural network fundamentals is recommended.
What you actually do
- Introduction · 7 min; Section 1 · Source
- Neural networks Intro · 42 min; Section 2 · Source
- Setting up the environment · 24 min; Section 3 · Source
- Minimal example · 20 min; Section 4 · Source
- Introduction to TensorFlow 2 · 24 min; Section 5 · Source
- Deep nets overview · 25 min; Section 6 · Source
- Backpropagation (optional) · 1 min; Section 7 · Source
- Overfitting · 20 min; Section 8 · Source
- Initialization · 9 min; Section 9 · Source
- Optimizers · 21 min; Section 10 · Source
- Preprocessing · 15 min; Section 11 · Source
- Deeper example · 40 min; Section 12 · Source
- Business case · 43 min; Section 13 · Source
- Conclusion · 19 min; Section 14 · Source
- Course exam · 20 min; Section 15 · Source
What the credential is
- Credential type
- Provider-issued certification · 365datascience.com / deep-learning-with-tensorflow-2
- Credential
- Course Certificate · 365datascience.com / certificates
- Issuer
- 365 Data Science · 365datascience.com / certificates
- Sharing
- Verifiable credential with Credential ID and Credential Link; sharable on LinkedIn/resumes; downloadable upon passing. Certificates are included with the Self-study learning plan. · 365datascience.com / certificates
- Exam requirement
- Pass the course exam; general certificate terms state 60% or above. Certificates are included with the Self-study learning plan. · 365datascience.com / certificates
- Credential expires
- provider does not state · 365datascience.com / deep-learning-with-tensorflow-2
- Renewal
- provider does not state · 365datascience.com / deep-learning-with-tensorflow-2
Who should skip it
- Check the course-specific preparation before enrolling: Python (version 3.8 or later), TensorFlow 2 library, and a code editor or IDE (e.g., Jupyter Notebook, VS Code, or Google Colab) Intermediate Python and machine learning knowledge is required. Familiarity with NumPy and neural network fundamentals is recommended. · Official source · 2026-09-27
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Change log
- : Initial recorded review of official program details. · Source
Cost details
The course requires Python 3.8+, TensorFlow 2, and an IDE. No course-specific completion price is established; access is provided via platform-wide subscription plans.
Official source · 2026-09-27