TensorFlow 2.0 Tutorials
What is Tensorflow 2.0?
Currently, the most famous #DeepLearning library in the world is Google’s TensorFlow. Google product uses machine learning in all of its products to improve the search engine, translation, image captioning, or recommendations.
Tensorflow architecture works in three parts:
Preprocessing the data
Build the model
Train and estimate the model
It is called Tensorflow because it takes input as a multi-dimensional array which is also known as tensors.
I am assuming that you know a little about machine learning and deep learning
Why Every Data Scientist Learn Tensorflow 2.x not Tensorflow 1.x
There are many changes in TensorFlow 2.0 to make users more productive, including removing redundant APIs, making APIs more consistent (Unified RNNs, Unified Optimizers), and better integrating with the Python runtime with Eager execution
No more globals
Functions, not sessions (session.run())
Use Keras layers and models to manage variables
It is faster
It takes less space
What Will I Learn?
- How to use Tensorflow 2.0 in Data Science
- Important differences between Tensorflow 1.x and Tensorflow 2.0
- How to implement Artificial Neural Networks in Tensorflow 2.0
- How to implement Convolutional Neural Networks in Tensorflow 2.0
- How to implement Recurrent Neural Networks in Tensorflow 2.0
- How to build your own Transfer Learning application in Tensorflow 2.0
- How to build a stock market trading bot using Reinforcement Learning (Deep-Q Network)
- How to build Machine Learning Pipeline in Tensorflow 2.0