Deep Learning with Neural Networks and TensorFlow Introduction
Why Stack Overflow recommends it
Stack Overflow users link it as a beginner-friendly starting point for neural networks and TensorFlow, both for newcomers told to build a simple network first and as the tutorial that askers followed before hitting problems.
Recommended by 1 people across 1 different months, 2017 to 2018.
What it is
An introductory video from sentdex that opens his Deep Learning with Neural Networks and TensorFlow tutorial series. It gives a short history of artificial neural networks, from the 1940s through the SVM era to the deep learning revival, and sets up the later TensorFlow coding episodes.
What people said
Recommends dv3 · 2017-03 · Stack Overflow · on “Predict next time for repeating event with tensorflow”
that is too broad of a question for StackOverflow. Try to specify it using this guide [LINK] . But essentially you want to do a regression on the delta between time_of_order and last_time_of_order. That's your y. Then you have your features using category of product etc (your x). Now you have a wide world of statistical analysis at your disposal. If you insist on using deep-learning: Try setting up a "simple" neural network using a [this video] . When you succeed, you can try using your own data. And you if you encounter problems... come back to StackOverflow with a specific programming question :) Have fun!
Cites Souvik Saha Bhowmik · 2017-06 · Stack Overflow · on “What should be the activaiton function for Neural Network in case of regression?”
My question is based on the understanding from [this video] In Neural Network a Neuron is activated by a threshold(activation) function, which in the above example is a sigmoid function. For regression problems do we need an activation function? regards Souvik
Cites Ramon Zhi · 2018-08 · Stack Overflow · on “Why does pycharm occupies more and more memory while debugging a TensorFlow program?”
I followed ( sentdex's Youtube video [this video] ) to create a TensorFlow program and it worked well. I started to tweak some parameters(converting all float32 to float 16 to try to make it faster) in it and got errors. So I tried to debug it with Pycharm. Then I discovered that during debug, even if I was pausing on one line and there was no other process running at the same time, python2.7 keeps occupying more and more memory until it hits 100% and my computer freezes. Is it that TensorFlow should not be debugged this way? Or is it that something else is wrong? import tensorflow as tf import time import numpy as np from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.r