Getting started with TensorFlow 2

Go to Course: https://www.coursera.org/learn/getting-started-with-tensor-flow2

Introduction

### Course Review: Getting Started with TensorFlow 2 Are you eager to dive into the world of deep learning? If so, "Getting Started with TensorFlow 2" on Coursera might just be the perfect course to help you get your feet wet. This course not only offers a fundamental understanding of TensorFlow 2 but also provides a practical, hands-on approach to building and training deep learning models. #### Overview TensorFlow is a cornerstone of modern machine learning, and this course teaches you the complete end-to-end workflow for developing deep learning models using TensorFlow. You'll learn how to build, train, evaluate, and make predictions with models using the Sequential API. The course emphasizes practical coding tutorials that allow you to apply what you’ve learned immediately. With a solid mix of theory and practice, it’s suitable for both beginners and those looking to refresh their knowledge. #### Syllabus Breakdown 1. **Introduction to TensorFlow** In the first week, you’ll get acquainted with TensorFlow and the course framework. The focus is on helping you set everything up, including familiarizing yourself with Google Colab, a handy tool for coding. This foundational knowledge is key as you prepare to dive into TensorFlow's capabilities. 2. **The Sequential Model API** This week introduces the high-level Keras API, empowering you to build and train models quickly. You will develop an image classification model from scratch using the MNIST dataset, one of the most popular datasets for beginners. The assignment ensures you have hands-on experience with the concepts learned. 3. **Validation, Regularisation, and Callbacks** This crucial week focuses on preventing overfitting—an essential skill in machine learning. You’ll learn about model validation techniques, regularisation methods, and how to implement callbacks to monitor and enhance performance. The assignment using the Iris dataset reinforces these concepts, allowing learners to put validation and regularisation into practice. 4. **Saving and Loading Models** Discovering how to save and load TensorFlow models is the focus here. You’ll gain skills in using callbacks for saving models, manual saving techniques, and understanding different options—including saving only model weights. An engaging assignment with satellite images will challenge you to implement what you learned practically. 5. **Capstone Project** The capstone project is a fantastic culmination of everything you've learned. It challenges you to develop a deep learning classifier on a labeled dataset of street view house numbers, allowing you to truly showcase your newly acquired skills and solidify your understanding of the entire workflow. #### Recommendation "Getting Started with TensorFlow 2" is highly recommended for anyone keen on exploring deep learning. The course's structure allows for a smooth transition from theory to practice, ensuring you not only learn but also apply your knowledge effectively. The combination of comprehensive tutorials, real-world applications, and supportive resources makes this course a great entry point into deep learning. Whether you're a student, a professional looking to pivot into AI, or simply an enthusiast, the insights gained from this course will be invaluable. Moreover, the course is self-paced, enabling you to learn at your convenience while still offering practical assignments that provide immediate feedback. Overall, if you want a robust introduction to deep learning with TensorFlow, this course is certainly worth your time and investment. Happy learning!

Syllabus

Introduction to TensorFlow

TensorFlow is one of the most popular libraries for deep learning, and it’s widely used today amongst researchers and professionals at all levels. In this week, you will get started with using TensorFlow on the Coursera platform and familiarise yourself with the course structure. You will also learn about some helpful resources when developing deep learning models in TensorFlow, including Google Colab. This week is really about getting everything set up, ready for diving into TensorFlow in the following week of the course.

There are multiple ways to build and apply deep learning models in TensorFlow, from high-level, quick and easy-to-use APIs, to low-level operations. In this week you will learn to use the high-level Keras API for quickly building, training, evaluating and predicting from deep learning models. The programming assignment for this week will give you the opportunity to put all this into practice and develop an image classification model from scratch on the MNIST dataset of handwritten images.

Model validation and selection is an essential part of developing any machine learning model development to help prevent overfitting and improve generalisation. In this week you will learn how to use a validation dataset in a training run and apply regularisation techniques to your model. You will also learn how to use callbacks to monitor performance and perform actions according to specified criteria. In the programming assignment for this week you will put model validation and regularisation into practice on the well-known Iris dataset.

As part of your deep learning model development, you will need to be able to save and load TensorFlow models, possibly according to certain criteria you want to specify. In this week you will learn how to use callbacks to save models, manual saving and loading, and options that are available when saving models, including saving weights only. In addition, you will practice loading and using pre-trained deep learning models. In the programming assignment for this week you will write flexible model saving and loading implementations for a model trained on satellite images.

In this course you have learned an end-to-end workflow for developing deep learning models in Tensorflow. The Capstone Project gives you the opportunity to bring all of your knowledge together to develop a deep learning classifier on a labelled image dataset of street view house numbers.