Probabilistic Programming Using TensorFlow Probability. This talk will teach you when, why and how to use TensorFlow probability. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions.

TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions.

Probabilistic programming allows us to encode domain knowledge to understand data and make predictions. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and Deep Learning. With TensorFlow 2.0, TFP can be very easily integrated into your code with very few changes and the best part - it even works with tf.keras!

This talk will teach you when, why and how to use TensorFlow probability.

Machine Learning With Python, Jupyter, KSQL, and TensorFlow. This post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers.

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with Python

Libraries play an important role when developers decide to work in Machine Learning or Deep Learning researches. In this article, we list down 10 comparisons between TensorFlow and PyTorch these two Machine Learning Libraries.