TensorFlowDeep Learning with TensorFlow Training
Build, train and deploy deep learning models with TensorFlow and Keras in 28 live hours
- Duration
- 28 hours
- Format
- Live online
- Modules
- 9 modules
- Certificate
- On completion
- ✓28 hours of live online instructor-led training in scheduled batches
- ✓TensorFlow 2 and Keras: models, layers, callbacks and custom training loops
- ✓Image, text and time-series projects
- ✓tf.data input pipelines, mixed precision and performance basics
- ✓Deployment with TensorFlow Serving and TensorFlow Lite
- ✓Assure Success course-completion certificate
About the course
What this course covers
Deep Learning with TensorFlow focuses on the TensorFlow and Keras ecosystem for building and deploying neural networks. If you have learned the basics of machine learning and want a production-oriented toolkit with strong mobile and edge options, this course gives you a practical, project-driven route.
You work through the Keras Sequential, Functional and subclassing APIs, build efficient data pipelines with tf.data, train convolutional and recurrent models, apply transfer learning from pretrained networks, and write custom training loops with GradientTape. The final modules cover saving models, serving them with TensorFlow Serving, converting them to TensorFlow Lite for mobile and edge devices, and tracking experiments with TensorBoard.
A note on certification: Google's TensorFlow Developer Certificate exam was suspended in 2024 and is not currently available, so this course does not prepare you for an external exam. You receive an Assure Success course-completion certificate after finishing a capstone.
Who should attend
- ● Data scientists and ML engineers who want to productionise neural networks
- ● Software and mobile developers interested in on-device AI
- ● Engineering graduates targeting AI and computer vision roles
- ● Practitioners who learned deep learning concepts and now need a framework
- ● Teams maintaining existing TensorFlow / Keras codebases
Prerequisites
Key skills
Skills you will walk away with
Keras model building
Create models with the Sequential, Functional and subclassing APIs.
Input pipelines
Use tf.data to load, augment, cache and prefetch data efficiently.
Computer vision
Train CNNs and apply transfer learning with pretrained backbones.
Text and sequence models
Build embeddings, LSTM and transformer-style models for text and series.
Custom training
Write custom losses, metrics and GradientTape training loops.
Callbacks and tracking
Use checkpointing, early stopping and TensorBoard to manage training.
Curriculum
Module-by-module syllabus
9 modules · 36 topics · 28 hours
1Module 1: TensorFlow and Keras Basics4 topics ▾
Learning objective: Get productive with the framework.
- ▸ Tensors, variables and eager execution
- ▸ Keras Sequential and Functional APIs
- ▸ Compile, fit and evaluate
- ▸ Overview of Keras 3 and available backends
2Module 2: Data Pipelines with tf.data4 topics ▾
Learning objective: Feed models efficiently.
- ▸ Datasets from arrays, files and generators
- Map, batch, shuffle, cache, prefetch
Schedules
Upcoming live batches
Enroll now and choose your batch dates
Send an enquiry and we'll reserve your seat in the next batch.
Certification
How you get certified
- 1Enrol in a scheduled live batch and prepare a Python and Colab environment
- 2Attend sessions and complete the project exercises
- 3Complete the capstone and present it for review
- 4Meet the attendance and assessment criteria for the batch
- 5Receive your Assure Success course-completion certificate
Career outcomes
Roles this opens up
Deep Learning Engineer
₹7–18 LPA (approx.)Builds and deploys neural network models in TensorFlow/Keras.
Machine Learning Engineer
₹7–20 LPA (approx.)Owns the training-to-serving pipeline for ML products.
Computer Vision Engineer
₹7–18 LPA (approx.)Develops image-based models, including on-device inference.
Edge AI / Mobile ML Developer
₹6–15 LPA (approx.)Brings compact models to phones and embedded devices using TensorFlow Lite.
Salary ranges are approximate figures for India and vary by city, company and experience.
Why this certification is worth it
Deployment-oriented
You learn to take a model from notebook to a serving or on-device format.
Why Assure Success
Learn it once, pass it once
Trainers from the field
Every session is led by a practitioner who uses this material at work.
Batches that fit your week
Weekday and weekend batches in IST, scheduled for working professionals.
Exam-style practice
Practice questions and, where available, a full simulator free with the course.
Exam voucher help
Discounted vouchers and step-by-step help booking your exam.
Human support
WhatsApp support Mon–Sat, 9 AM–8 PM IST — a person answers.
Pay your way
UPI, cards, netbanking and EMI in India; international cards accepted.
FAQs
Questions learners ask us
Q.Does this prepare me for the TensorFlow Developer Certificate?
That exam was suspended by the TensorFlow team in 2024 and is not currently offered. The course teaches the same kind of skills, but there is no external exam to book.
Q.TensorFlow or PyTorch: which should I learn?
PyTorch is dominant in research and many new projects; TensorFlow/Keras is widely used in existing production systems and for mobile deployment with TensorFlow Lite. The concepts transfer between them.
Q.Is a GPU required?
No. We use free GPU notebooks such as Google Colab.
Q.I have not studied machine learning. Can I join?
It is better to complete a machine learning course first. You should understand overfitting, validation and basic metrics.
Q.How does this differ from the Deep Learning Certification Training?
That course (40 hours) is framework-neutral and goes deeper into theory and transformers. This one (28 hours) is TensorFlow-focused with more emphasis on pipelines and deployment.
Q.Do I get a recognised certificate?
You receive an Assure Success course-completion certificate. It records what you completed; it is not an exam body credential.
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₹35,499
Deep Learning with TensorFlow Training