About Tensorflow

TensorFlow is an open-source software library for numerical computation using data flow graphs. It is used for machine learning and artificial intelligence. TensorFlow was developed by the Google Brain team at Google. It is available under the Apache 2.0 license.

TensorFlow can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. It can be used on a variety of platforms, including CPUs, GPUs, and TPUs. TensorFlow is used by a wide range of companies and organizations, including Google, Facebook, and Uber.

Some of the benefits of using TensorFlow include:

• It is open-source and free to use.
• It is highly scalable and can be used to train and deploy large models.
• It is well-documented and has a large community of users and developers.
• It is available for a variety of platforms.

Some of the challenges of using TensorFlow include:

• It can be complex to learn and use.
• It can be difficult to debug.
• It can be memory-intensive.

Here are some of the things that TensorFlow can be used for:

• Image recognition
• Natural language processing
• Speech recognition
• Machine translation
• Robotics
• Recommender systems
• Fraud detection
• Medical diagnosis
• Financial forecasting

Articles

Introduction

• Tensorflow
• Install
• Prepare and load data for successful ML outcomes
• Build and fine-tune models with the ecosystem
• Deploy models on-device, in the browser, on-prem, or in the cloud
• Implement MLOps for production ML
• TensorFlow Core
• TensorFlow 2
• Load a dataset 
• Build a machine learning model
• Train and evaluate your model

ML Basics with Keras 

• ML Basics with Keras

Image classification

1. Basic image classification
2. Import the MNIST dataset
3. Explore the data
4. Preprocess the data
5. Build the model
6. Set up the layers
7. Compile the model
8. Train the model
9. Feed the model
10. Evaluate accuracy
11. Make predictions
12. Verify predictions
13. Use the trained model

Text classification

1. Basic text classification
2. Sentiment analysis
3. Download and explore the dataset
4. Load the dataset
5. Prepare the dataset for training
6. Configure the dataset for performance
7. Create the model
8. Loss function and optimizer
9. Evaluate the model
10. Create a plot of accuracy and loss over time
11. Export the model
12. Inference on new data
13. Text classification with TensorFlow Hub: Movie reviews

Regression

1. Regression
2. Basic regression: Predict fuel efficiency
3. Get the data
4. Clean the data
5. Split the data into training and test sets
6. Inspect the data
7. Split features from labels
8. Linear regression
9. Linear regression with one variable
10. Linear regression with multiple inputs
11. Regression with a deep neural network (DNN)
12. Regression using a DNN and a single input
13. Regression using a DNN and multiple inputs
14. Performance

Normalization

1. Normalization
2. The Normalization layer