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Practical Deep Learning For Coders, Part 1

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This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.


  1. Recognizing cats and dogs
  2. Convolutional neural networks
  3. Why deep learning. Intro to convolutions
  4. CNN architecture basics. Avoiding over and under-fitting
  5. CNN/SGD in Excel. Pseudo-labeling. Collaborative filtering
  6. Intro to NLP, keras functional API, and RNNs
  7. Embeddings in Excel. Building RNNs
  8. Exotic CNN architecures. RNN from scratch

Taught by

Jeremy Howard

Reviews for Practical Deep Learning For Coders, Part 1
4.0 Based on 6 reviews

  • 5 stars 67%
  • 4 star 0%
  • 3 star 17%
  • 2 star 0%
  • 1 star 17%

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  • 1
Eric P
5.0 a year ago
by Eric is taking this course right now, spending 15 hours a week on it and found the course difficulty to be medium.
I've seen many different Machine Learning courses from "big-league" Udacity/Coursera & co to small YouTube players like SentDex or Siraj_Raval. Also completed the Udacity Data Analyst nanodegree with a Machine Learning module taught by a Google Brain scientist.

This is a superb one, full with hands-on cases and support explanations of Neural Networks on Excel (Yes, Excel FFS !) by Jeremy Howard (ex-Kaggle CEO).

It relies on Python 2.7 + Theano + Keras + Jupyter Notebook. And Part II with Python 3.5 +Tensorflow + Keras is about to be released.

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5.0 a year ago
Anonymous is taking this course right now.
This is really a hidden gem in a field that rapidly growing.

Jeremy Howard does an excellent job of both walking through the basics (going as far as to prototype Stochastic Gradient Descent algorithms in Excel), and presenting state of the art results. I was surprised time and again when not only was he presenting material developed within the last year, but even within the week the course was running. The courses use of Keras as a high level API for Theano/Tensorflow is great for abstracting and conceptualizing the architecture of the deep learning models.

You not only get a walkthrough of how to use AWS for GPU leveraged learning, but you practice on real life data through Kaggle competitions. I would strongly recommend this course to anyone looking to go from zero real world experience to competing with experts in the field.
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Satish K
5.0 a year ago
by Satish completed this course.
I haven't found a better course for hands on deep learning. Jeremy covers all the practical aspects of DL, and covers the theoretical aspects without being intimidating. Highly recommended. Especially for people without heavy ML/DL background, but would like to incorporate the latest DL techniques in their domain
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Bibhash T
5.0 a year ago
by Bibhash is taking this course right now.
The only course that takes this code-centric approach to deep learning. Highly recommended for anyone who don't understand intuitions from mathematical formulae.
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Mark M
1.0 a year ago
Mark dropped this course, spending 10 hours a week on it and found the course difficulty to be easy.
Extremely difficult to get install up and working. There are many "known issues" with the install video tutorial, but the video hasn't been remade and the known issues aren't collected in one place, resulting in hours wasted, scouring the forums to get install working. Speaking of the forums, the organization of threads is poor and no one seems to be monitoring the forums for new questions -- most of my questions went unanswered. I have had much better luck with other online classes where people are moving through as a cohort and so are reading and answering each other's questions on the same week's content. There may be great material in here, but the barrier to entry is too high. I was forced to give up after week one.
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Cyril O
3.0 2 months ago
Cyril partially completed this course.
I personaly think that it is not as great as people say it is. I have taken the famous machine learning class on coursera to get a good base from where I could go into practice. So I have a good basic understanding of the theory. So I went for this one for practice but so far I am disapointed because nothing is never clearly explained. I end up spending a huge amount of time to fill the holes. In one word: NOT CLEAR and confusing. I move on...
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