Скачать или смотреть getting started with tensorflow 2 0 google i o 19

Download 1M+ code from https://codegive.com/b3c0707
getting started with tensorflow 2.0 - google i/o 2019

tensorflow 2.0 is a significant update to google's popular open-source machine learning library. it emphasizes ease of use, flexibility, and performance, making it more accessible for beginners and powerful for advanced users. this tutorial will guide you through the basics of tensorflow 2.0, including installation, key features, and a simple example.

1. installation

you can install tensorflow 2.0 directly using pip. open your terminal or command prompt and run:



to verify the installation, you can run the following python code in your terminal or in a jupyter notebook:



you should see `2.0.0` printed as the output.

2. key features of tensorflow 2.0

**eager execution**: tensorflow 2.0 runs operations immediately as they are called from python, which makes debugging and iteration easier.
**keras integration**: tensorflow 2.0 comes with keras, a high-level api for building and training neural networks, making it easier to create deep learning models.
**functionality**: it supports both high-level apis for building models and low-level apis for more advanced users.
**tf datasets**: a higher-level api for loading and preprocessing datasets.

3. simple example: building a neural network

in this example, we'll create a simple neural network using tensorflow 2.0 to classify the mnist dataset, which consists of handwritten digits.

step 1: import libraries



step 2: load the dataset

the mnist dataset is available directly in keras.



step 3: build the model

we will create a simple feedforward neural network with one hidden layer.



step 4: compile the model

we need to specify the optimizer, loss function, and metrics for our model.



step 5: train the model

now we can train the model using the training data.



step 6: evaluate the model

after training, we can evaluate the model's performance on the test dataset.



step 7: make predictions

you can use the trained model to make pred ...

#TensorFlow #GoogleIO19 #windows
tensorflow 2.0
Google I/O 2019
machine learning
deep learning
neural networks
AI development
TensorFlow tutorials
model training
data science
TensorFlow features
coding in TensorFlow
TensorFlow ecosystem
beginner TensorFlow
TensorFlow applications
programming with TensorFlow