#
Data Science

Deep Learning and Neural Networks in Python

Below are the **top discussions** from
Reddit that mention this
online Udemy course.

The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code

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Taught by

**Lazy Programmer Inc.**

#### Reddit Posts and Comments

0 posts • 2 mentions • top 2 shown below

**r/neuralnetworks**• comment

1 points • sidewayskumara

Hey so i haven’t seen any thing that specifically does what you want, but if it was me I would break what you’re trying to learn into two separate things. First I would say you should learn backprop individually as it applies to all NN, in theory and code, here is my fav course where he takes you through coding backprop in numpy:

https://www.udemy.com/course/data-science-deep-learning-in-python/?referralCode=4B846F3BB454BE9DDB7F

The same guy has a course on CNNs and a recent Tensorflow 2.0 course which includes a practical explanation on how to architect CNNs. His slides can be skeletal but there is plenty of free material online to supplement them.

Are you trying to literally do convolution and pooling etc. from scratch? With his code you can see that he writes out each layer of the network so I guess you can apply convolution (actually cross correlation) to the first layer. I think from an explanation of how CNNs work it shouldn’t be too hard to figure out the packages and functions you’d need but it would be tedious as hell. What are you trying to achieve in the end? Anyway hope that helps somewhat.

**r/learnmachinelearning**• comment

1 points • TaplierShiru

Hi!

Here is a list of tutorials which I went through some years ago with my comments:

I think this is what you searching for. About CNN (Some theory and practice) + examples of their usage in popular frameworks (TensorFlow, PyTorch and etc...) - https://www.udemy.com/course/deep-learning-convolutional-neural-networks-theano-tensorflow/

But my recommendation is to start with some other basic tutorial. This is tutorial about logistic regression and other very low-stuff for neural network (https://www.udemy.com/course/data-science-logistic-regression-in-python/). This is my first tutorial by this Author and I love it. In some case this tutorial can be hard at the beginning because most of the code and the math for the neural network are written from scratch with a python library - Numpy (but with some practice, I'm sure you can learn this library very quickly). Tutorials are not free, but it worth it. It can gives you very good understanding what happening inside a neural network, can give you very strong background of the knowledge (at least for me it is true).

Other tutorials by this author dives deeper into math and theory for different types of the neural network and I really love them. They gave me a lot of useful knowledge, also I recommend to you other tutorials by this author:

https://www.udemy.com/course/data-science-deep-learning-in-python/

https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/

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There are frequent discounts on these tutorials which i recommended to you (I think every month). Discounts are available right now, so you can try to buy. If something goes wrong, you can get your money back.

I hope this can helps you, as it helped me once, good luck!

(Also sorry for my poor english, I'm still learning)