TensorFlow Tutorial
Last Updated : 13 Feb, 2025
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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:
TensorFlow Introduction
- What is TensorFlow?
- Architecture of TensorFlow
- Difference between TensorFlow and Keras
- TensorFlow installations in macOS
- TensorFlow installations in Windows
- TensorFlow installations in Linux
You can install TensorFlow using the following command:
pip install tensorflow
TensorFlow installations using pip GPU version:
pip install tensorflow-gpu
To verify the installation:
import tensorflow as tf
print('TensorFlow version - ',tf.__version__)
# Check if GPU is available
gpu_available = tf.config.list_physical_devices('GPU')
if gpu_available:
print("TensorFlow is installed as GPU version.")
else:
print("TensorFlow is installed as CPU version.")
Output:
TensorFlow version - 2.18.0
TensorFlow is installed as CPU version.
Basic Concepts of TensorFlow
- Tensor in TensorFLow
- Tensor Indexing
- Tensor Reshaping
- Tensor Broadcasting
- Ragged Tensors in TensorFlow
- Sparse Tensors in TensorFlow
- String Tensors in TensorFlow
- Variables in TensorFlow
- Automatic Differentiation in TensorFlow
- Graphs and Functions in TensorFlow
- Bitwise Operations in TensorFlow
- Numerical Operations in TensorFlow
Model Building and Training in TensorFlow
- TensorFlow Keras API
- Building a Neural Network using TensorFlow
- Single Layer Perceptron
- Multi-Layer Perceptron Learning
- Neural Network Layers in Tensorflow
- Activation Functions in TensorFlow
- Loss Functions in TensorFlow
- Optimizers in TensorFlow
- Backpropagation
- Gradient Descent
- model.evaluate()
- model.fit() in TensorFlow
- Evaluation Metrics in TensorFlow
- Saving and Loading Models
- Callbacks
- How to visualize training progress in TensorFlow?
Natural Language Processing (NLP) with TensorFlow
- Text Preprocessing in TensorFLow
- TF-IDF Representations in TensorFlow
- Bag-of-Words Representations in TensorFlow
- Recurrent Layers in TensorFlow
- Recurrent Neural Networks (RNN) in TensorFlow
- Attention Layers in TensorFlow
- Transformer Model in TensorFlow from Scratch
- Text Classification Using RNNs
- Text Generation using LSTM
- Detecting Spam Emails Using Tensorflow
- Sentence Autocomplete Using TensorFlow
TensorFlow for Computer vision Tasks
- Convolution Layers in TensorFlow
- Convolutional Neural Network (CNN) in Tensorflow
- Image data augmentation with TensorFLow
- Image Classifications using TensorFlow
- Object Detection using TensorFlow
- FaceMask Detection using TensorFlow
- Image segmentation using TensorFlow
- Deep Convolutional GAN for Image generations in TensorFlow
Applications of TensorFlow
TensorFlow'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.
