Tensorflow Tutorial Geeksforgeeks

Leo Migdal

-Nov 17, 2025, 9:04 AM

TensorFlow is an open-source machine-learning framework developed by Google. It is written in Python, making it accessible and easy to understand. It is designed to build and train machine learning (ML) and deep learning models. Before starting TensorFlow, a strong foundation in key concepts will help you understand and use the framework effectively. Here are the essential prerequisites for our tutorials: For installation of tensorflow you can refer to:

TensorFlow's versatility extends across a vast array of real-world applications: TensorFlow is an open-source framework for machine learning (ML) and artificial intelligence (AI) that was developed by Google Brain. It was designed to facilitate the development of machine learning models, particularly deep learning models by providing tools to build, train and deploy them across different platforms. It supports a wide range of applications from natural language processing (NLP) and computer vision (CV) to time series forecasting and reinforcement learning. TensorFlow is designed to scale across a variety of platforms from desktops and servers to mobile devices and embedded systems. It supports distributed computing allowing models to be trained on large datasets efficiently.

TensorFlow offers a broad set of tools and libraries including: TensorFlow automatically calculates gradients for all trainable variables in the model which simplifies the backpropagation process during training. This is a core feature that enables efficient model optimization using techniques like gradient descent. TensorFlow is primarily designed for Python but it also provides APIs for other languages like C++, Java and JavaScript making it accessible to developers with different programming backgrounds. TensorFlow is an open-source powerful library by Google to build machine learning and deep learning models. The huge ecosystem of TensorFlow will make it easier for everyone in developing, training and deployment of scalable AI solutions.

TensorFlow cheat sheet helps you on immediate reference to commands, tools, and techniques. Whether you are a beginner or an experienced developer, this guide will streamline your workflow and boost your productivity with TensorFlow. In this article , Tensor Cheat-Sheet provides a concise overview of key commands and Techniques; TensorFlow is a free and open-source machine learning framework developed by Google mainly used to build, train and deploy machine learning and deep learning models. It supports numerous tasks such as image recognition and natural language processing. It is available on both CPU, GPU and TPU without hiccups and the easy-to-use in Keras API.

A "TensorFlow cheat sheet" is a convenient reference guide giving easy and ready access to key commands, functions and techniques. It forms a useful pocket guide for programmers, data scientists and Machine Learning enthusiasts to make life easier by compressing the main features of core TensorFlow into their workflow. Download the Cheat-Sheet Here- Tensorflow Cheat-Sheet Deep learning has been on the rise in this decade and its applications are so wide-ranging and amazing that it's almost hard to believe that it's been only a few years in its advancements. And at the core of deep learning lies a basic "unit" that governs its architecture, yes, It's neural networks. A neural network architecture comprises a number of neurons or activation units as we call them, and this circuit of units serves their function of finding underlying relationships in data.

And it's mathematically proven that neural networks can find any kind of relation/function regardless of its complexity, provided it is deep/optimized enough, that is how much potential it has. Now let's learn to implement a neural network using TensorFlow Tensorflow is a library/platform created by and open-sourced by Google. It is the most used library for deep learning applications. Now, creating a neural network might not be the primary function of the TensorFlow library but it is used quite frequently for this purpose. So before going ahead let's install and import the TensorFlow module.

Using the pip/conda command to install TensorFlow in your system TensorFlow eğitimleri Jupyter not defterleri olarak yazılmıştır ve hiçbir kurulum gerektirmeyen, barındırılan bir not defteri ortamı olan Google Colab'da doğrudan çalıştırılır. Her eğitimin üst kısmında Google Colab'da Çalıştır düğmesini göreceksiniz. Not defterini açmak ve kodu kendiniz çalıştırmak için düğmeye tıklayın. 이 문서는 구글 코랩(Colaboratory) 노트북 파일입니다. 파이썬 프로그램을 브라우저에서 직접 실행할 수 있기 때문에 텐서플로를 배우고 사용하기 좋은 도구입니다:

MNIST 데이터셋을 로드하여 준비합니다. 샘플 값을 정수에서 부동소수로 변환합니다: 참고: 자체 개발 환경을 사용하는 경우에 TensorFlow 2 패키지를 설치하려면 최신 pip로 업그레이드했는지 확인합니다. 자세한 내용은 설치 가이드를 참조합니다. MNIST 데이터세트를 로드하고 준비합니다. 샘플 데이터를 정수에서 부동 소수점 숫자로 변환합니다.

층을 차례대로 쌓아 tf.keras.Sequential 모델을 만듭니다. 훈련에 사용할 옵티마이저(optimizer)와 손실 함수를 선택합니다: Keras high-level neural networks APIs that provide easy and efficient design and training of deep learning models. It is built on top of powerful frameworks like TensorFlow, making it both highly flexible and accessible. Keras has a simple and user-friendly interface, making it ideal for both beginners and experts in deep learning. Keras simplifies the process of building and training deep learning models while abstracting away complex underlying operations.

This tutorial covers everything you need to know to get started with Keras, from installation to advanced topics, making it a perfect guide for those looking to dive into deep learning Keras is simple to install as part of TensorFlow. You can install it using pip, making it easy to get started with deep learning models right away. This section will guide you through installation steps on various operating systems. Keras is a high-level neural networks API designed to simplify the process of building and training deep learning models. It’s built on top of powerful frameworks like TensorFlow and provides an easy-to-use interface, making it accessible to both beginners and experts

Training a model in Keras involves preparing your data, defining a model and specifying the number of epochs. Keras simplifies the training process with built-in methods for monitoring performance, adjusting hyperparameters and saving the trained model. TensorFlow is an open source machine learning framework for all developers. It is used for implementing machine learning and deep learning applications. To develop and research on fascinating ideas on artificial intelligence, Google team created TensorFlow. TensorFlow is designed in Python programming language, hence it is considered an easy to understand framework.

This tutorial has been prepared for python developers who focus on research and development with various machine learning and deep learning algorithms. The aim of this tutorial is to describe all TensorFlow objects and methods. Before proceeding with this tutorial, you need to have a basic knowledge of any Python programming language. Knowledge of artificial intelligence concepts will be a plus point.

Tensorflow Tutorial Geeksforgeeks

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