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Week 7 is the last week of this deep-learning plan. And I didn’t want to finish before trying a bit more to have better results. So here we go again. This time I learned two things: Google Colaboratory and Lambda layer. Those two have helped me refactor my code in two notebooks to get better results. Google Colaboratory Because my computer was so slow to process such dataset, I didn’t have the luxury of experimenting with models and hyperparameters. So one obvious step was to look for an online solution to process my python code with an acceptable speed. Google Colaboratory was a nice and free solution that fitted my needs. If you haven’t used it before, here is a  nice tutorial This processing power allowed me to rerun a previous notebook to train my own CNN for CIFAR-10 dataset. But for 100 epochs instead of only 15. This allowed me to see how my model was performing well enough to reach an accuracy of 82.68%. (compared to 74.53% with 15 epochs) You can find th...

Here I come to the 7th and final week of the initial plan to start working with deep learning. This week’s assignment was to use an existing CNN to learn the CIFAR-10 dataset. The VGG16 model was chosen for me to try. It was a quite interesting assignment to find some problems I will probably be facing again in the future. Let’s talk about what I did this week. You can find the whole python notebook on Github  here . My images are small This was the first problem I faced. According to the Keras documentation , the model takes a minimum size of 32px. But actually when I tried, it was a minimum of 48px. But my dataset had images of size 32px. So I had to resize the images. This turned out to take more time that expected. There were a few solutions for resizing such data. And to find a suitable and less complicated solution, it took me quite some time to read the documentation of each one and try it. My computer is a humble one Not only did I have to resize the image...

Here I am in Week 5&6 of my mentor’s plan to practice deep-learning and start solving real problems. This time, my homework was about designing another CNN like the previous week but with the CIFAR-10 dataset. At first, I was intimidated and thought that I really sucked and that I cannot really move on by myself. But Which I think was close to being true. :D But after several hours of looking closely at the dataset and reading the Keras documentation, I started to find results! Here they are. You can find the whole python notebook on Github  here . UPDATE: I reran the code but for 100 epochs and could reach a  82.68%  accuracy. The notebook is found  here . The accuracy I could get this time compared to previous MNIST homework reminded me of the difference between my 9X% results at school compared to the embarrassing university’s results :D Anyways. I could get an accuracy of 74.53%. This already required a lot of time to train on my humble computer wi...

Week 2 of my deep learning plan was to train a convolutional network using the MNIST dataset. The intent is to learn the basics of convolutional networks. Instead of writing the code of my homework here again, I will only link to it on Github and speak about my experience with it here. You can find the whole python notebook on Github  here . This homework was the fist step of feeling not like the hello-world example in week 1. The first step was to load the MNIST data like before, analyzing the data and curating it before processing. It was not complicated but it was a nice start to realizing how important this step is for deciding how to design the network. Next was the mix of confusion and fun. It was basically about looking for the reasonable number of hidden layers and number of filters per layer. After finding the ‘popular’ numbers for such a problem (what if my problem is a personal/custom one? back to that later in the future inshallah), it was time to try differt dep...

Week 1 of the plan was to train a feedforward network using the MNIST dataset . This is probably the easiest and most straightforward example to understand how the training cycle goes. Before starting, I should mention that the code for this week -and the other weeks too- was not written from scratch by me. And that’s the main difference between school and work. It treated this homework the same way I treat work. I can google what I want and understand/refactor it. It can also be about reading about a specific network and understanding the recommended range of values for a specific parameter or the recommended layers structure. This time, it was too simple that I copied the code and started playing with it to understand how it works. And as we move forward in the weeks, I had to write more myself. Consider it a Hello World week. Now lets see the code! You can find the whole python notebook on Github  here . Resources: Victor Schmidt (Vict0rSch) on  Github   d...

Starting with my deep-learning learning plan, I wanted to have a good development environment. This meant having a Docker image that can work on any device I am using with no extra setup needed. My choice was to use Keras with Tensorflow core for an easy start with not so many unwanted details at this step. I also chose to use Jupyter notebook to have a nice interface to trace/explain my code along with graphs and output numbers. The following is my finalized Dockerfile with the latest versions which worked exactly how I wanted it. # To build the container # docker build -t jupyter-keras . # To run the container: # docker run -it -v /$(pwd)/:/home/jovyan/work -p 8888:8888 jupyter-keras:latest start-notebook.sh --NotebookApp.token='' # To access the notbook from the browser: # http://localhost:8888/tree # To login in to the server: # docker exec -it /bin/bash # To check Keras version: # python -c 'import keras; print(keras.__version__)' FROM jupyter/scipy-not...

I’ve been learning machine learning by myself for a long time. From one Coursera specialization to a course to a YouTube playlist. But then I felt the problem with starting. I am learning the theory with some basic applications, but I don’t know how to go on by myself and start a project and analyze the data and find the correct structure and find-tune the parameters and so on… Then came to me the old idea one more time; I need a mentor who knows how such professional life works and what really matters more. And after some searching and asking, I found one through a friend of a friend of a friend. And then she contacted me and offered help.    And after explaining to her what I know and what I want in life from this exercise, she formulated a plan for me. So here I am posting it. Week 1: Feedforward networks A good start is the simple MNIST dataset. So train a feedforward network to study the basics of neural networks. Week 2: Convolutional networks Chang...