arrow_back Information Retrieval using Deep Learning
Deep Learning with TensorFlow
Submitted by Abdul Muneer (@abdul-muneer) on Wednesday, 26 April 2017
Section: Workshop Technical level: Intermediate
TensorFlow is an open source software library for numerical computation using data flow graphs. Created by Google Brain, it was quickly adopted by the machine learning community after it was open sourced. Now it is adopted by industry pioneers like DeepMind at Google, OpenAI, IBM etc. and is fast becoming the go-to library for implementing deep neural networks.
This workshop aims to cover the core APIs, graph defenition and execution, tensorboard visualization, and implementation of Deep Learning Models with TensorFlow.
The workshop is intended for developers who know Python, Numpy and are at least remotely familiar with machine learning.
Introduction to TensorFlow
Building TensorFlow Graph
Tensors - tensorflow data types
Variables and Name scopes
Implement a Convolutional Neural Network (Convolution, non-linearity, pooling, fully connected)
The workshop will be conducted using Jupyter Notebooks. Participants should bring a system with Python 3, Numpy and Jupyter Notebook installed.
I implement deep neural network models using tensorflow at minds-ai. My focus is on segmentation neural networks with applications in smart cars and health care systems.
When I am free from day job, I also conduct corporate trainings on Python.