Friday, 14 November 2025

Getting started with TensorFlow 2

Python Developer November 14, 2025 Coursera, Deep Learning No comments

Introduction

Deep learning frameworks have become central tools in modern artificial intelligence. Among them, TensorFlow (especially version 2) is one of the most widely used. The course “Getting started with TensorFlow 2” helps you build a complete end‑to‑end workflow in TensorFlow: from building, training, evaluating and deploying deep‑learning models. It’s designed for people who have some ML knowledge but want to gain hands‑on competency in TensorFlow 2.

Why This Course Matters

TensorFlow 2 introduces many improvements (ease of use, Keras integration, clean API) over earlier versions — mastering it gives you a useful, modern skill.
The course isn’t just theoretical: it covers actual workflows and gives you programming assignments, so you move from code examples to real model building.
It aligns with roles such as Deep Learning Engineer or AI Practitioner: knowing how to build and deploy models in TensorFlow is a strong industry‑skill.
It’s part of a larger Specialization (“TensorFlow 2 for Deep Learning”), so it fits into a broader path and gives you credential‑value.

What You’ll Learn

Here’s a breakdown of the course content and how it builds your ability:

Module 1: Introduction to TensorFlow

You’ll begin with setup: installing TensorFlow, using Colab or local environments, understanding what’s new in TensorFlow 2, and familiarising yourself with the course and tooling.
This module gets you comfortable with the environment and prepares you for building models.

Module 2: The Sequential API

Here you’ll dive into model building using the Keras Sequential API (which is part of TensorFlow 2). Topics include: building feed‑forward networks, convolution + pooling layers (for image data), compiling models (choosing optimisers, losses), fitting/training, evaluating and predicting.
You’ll likely build a model (e.g., for the MNIST dataset) to see how the pieces fit together.

Module 3: Validation, Regularisation & Callbacks

Models often over‑fit or under‑perform if you don’t handle validation, regularisation or training control properly. This module covers using validation sets, regularisation techniques (dropout, batch normalisation), and callbacks (early stopping, checkpoints).
You’ll learn to monitor and improve model generalisation — a critical skill for real projects.

Module 4: Saving & Loading Models

Once you have a trained model, you’ll want to save it, reload it, reuse it, maybe fine‑tune it later. There’s a module on how to save model weights, save the full model architecture, load and use pre‑trained models, and leverage TensorFlow Hub modules.
This ensures your models aren’t just experiments — they become reusable assets.

Module 5: Capstone Project

Finally, you bring together all your skills in a Capstone Project: likely a classification model (for example on the Street View House Numbers dataset) where you build from data → model → evaluation → prediction.
This is where you apply what you’ve learned end‑to‑end and demonstrate readiness.

Who Should Take This Course?

Learners who know some machine‑learning basics (e.g., supervised learning, basic neural networks) and want to build deeper practical skills with TensorFlow.
Python programmers or data scientists who might have used other frameworks (or earlier TensorFlow versions) and want to upgrade to TensorFlow 2.
Early‑career AI/deep‑learning engineers who want to build portfolio models and deployable workflows.
If you're completely new to programming, or to ML, you might find some modules challenging—especially if you haven’t done neural networks yet—but the course still provides a structured path.

How to Get the Most Out of It

Set up your environment: Use Google Colab or install TensorFlow locally with GPU support (if possible) so you can run experiments.
Code along every module: When the videos demonstrate building a model, train it yourself, modify parameters, change the dataset or architecture and see what happens.
Build your own mini‑projects: After you finish module 2, pick a simple image dataset (maybe CIFAR‑10) and try to build a model. After module 3, experiment with over‑fitting/under‑fitting by adjusting regularisation.
Save, load and reuse models: Practise the workflow of saving a model, reloading it, fine‑tuning it or using it for prediction. This makes you production‑aware.
Document your work: Keep Jupyter notebooks or scripts for each exercise, record what you changed, what result you got, what you learned. This becomes your portfolio.
Reflect on trade‑offs: For example, when you change dropout rate or add batch normalisation, ask: what changed? How did validation accuracy move? Why might that happen in terms of theory?
Connect to real use‑cases: Think “How would I use this model in my domain?” or “How would I deploy it?” or “What data would I need?” This helps make the learning concrete.

What You’ll Walk Away With

By the end of the course you will:

Understand how to use TensorFlow 2 (Keras API) to build neural network models from scratch: feed‑forward, CNNs for image data.
Know how to train, evaluate and predict with models: using fit, evaluate, predict methods; understanding loss functions, optimisers, metrics.
Be familiar with regularisation techniques and callbacks so your models generalise better and training is controllable.
Be able to save and load models, reuse pre‑trained modules, and build reproducible model workflows.
Have one or more mini‑projects or a capstone model you can demonstrate (for example for your portfolio or job interviews).

Join Now: Getting started with TensorFlow 2

Conclusion

“Getting started with TensorFlow 2” is a well‑structured course for anyone wanting to gain practical deep‑learning skills with a major framework. It takes you from environment setup through building, training, evaluating and deploying models, and gives you hands‑on projects. If you’re ready to commit, experiment and build portfolios rather than just watch lectures, this course offers real value.