TensorFlow Tutorial Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report 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. It is highly scalable for both research and production.It supports CPUs, GPUs, and TPUs for faster computation.TensorFlow provides built-in tools for visualization and debugging.It works seamlessly with other AI frameworks and libraries.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:Python for Data Science Deep Learning Tutorial Math for Data Science TensorFlow IntroductionWhat is TensorFlow?Architecture of TensorFlowDifference between TensorFlow and KerasTensorFlow installations in macOSTensorFlow installations in WindowsTensorFlow installations in LinuxFor installation of tensorflow you can refer to: How to Install Python Tensorflow in Windows?Install Tensorflow on MacOSBasic Concepts of TensorFlow Tensor in TensorFLowTensor Indexing Tensor ReshapingTensor Broadcasting Ragged Tensors in TensorFlowSparse Tensors in TensorFlowString Tensors in TensorFlowVariables in TensorFlowAutomatic Differentiation in TensorFlowGraphs and Functions in TensorFlow Bitwise Operations in TensorFlowNumerical Operations in TensorFlowModel Building and Training in TensorFlowTensorFlow Keras APIBuilding a Neural Network using TensorFlowSingle Layer PerceptronMulti-Layer Perceptron LearningNeural Network Layers in TensorflowActivation Functions in TensorFlowLoss Functions in TensorFlowOptimizers in TensorFlowBackpropagationGradient Descent model.evaluate()model.fit() in TensorFlow Evaluation Metrics in TensorFlow Saving and Loading ModelsCallbacks How to visualize training progress in TensorFlow?Natural Language Processing (NLP) with TensorFlowText Preprocessing in TensorFLowTF-IDF Representations in TensorFlowBag-of-Words Representations in TensorFlowRecurrent Layers in TensorFlow Recurrent Neural Networks (RNN) in TensorFlowAttention Layers in TensorFlow Transformer Model in TensorFlow from ScratchText Classification Using RNNsText Generation using LSTM Detecting Spam Emails Using TensorflowSentence Autocomplete Using TensorFlowTensorFlow for Computer vision TasksConvolution Layers in TensorFlowConvolutional Neural Network (CNN) in TensorflowImage data augmentation with TensorFLowImage Classifications using TensorFlowObject Detection using TensorFlowFaceMask Detection using TensorFlowImage segmentation using TensorFlowDeep Convolutional GAN for Image generations in TensorFlowApplications of TensorFlowTensorFlow's versatility extends across a vast array of real-world applications:Image Recognition: Develop image classification models to identify objects, faces, or scenes in images.Natural Language Processing (NLP): Construct models for sentiment analysis, machine translation, or text summarization.Recommender Systems: Craft personalized recommendations for products, movies, or music based on user preferences and behavior.Time Series Forecasting: Predict future trends in time-based data, such as stock prices or weather patterns. 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