I always thought that deploying an app on the server is quite easy! I mean sure you will have some errors, you may spend few hours trying to make things work on the server, you may even do different steps from setting up the server, setting up the database, make code changes, etc and as much as I always enjoyed doing that, we have to admit that this is only valid for small projects. Companies or even small startups are always making changes and adding new features, so it's not smart to waste time each time they want to deploy something new. You may have heard about CI before, maybe CI/CD, so let's see today how we can make things more professional. In this blog I will deploy a Symfony 5 project, same steps should be valid for any other framework, we just have to create our docker container file and make some changes like port mapping... In a more professional context, we should have 2 environments to deploy our app, an environment for production which have the stable ver...

I've been fascinated with machine learning for a while, I did follow some tutorials, watch some videos, read some articles but I never find the time to focus on ML and get really good at it! This article will be the story of my road to become a Machine Learning expert. Being a Machine Learning expert is one of my goals for the year 2019. My curiosity about machine learning started the late of the year 2017,  I did follow some tutorials, participate in some ML hackathons, make some ML projects... so here is what I did so far : In the year 2017, I was watching Siraj Raval  Youtube channel, read articles, tried Udacity online course, Andrew ng tutorials... but to be honest, I was having a hard time to understand things, I was at the beginning of the journey and I didn't know the right path to start! This changed after I followed the following course in Udemy:  Machine Learning A-Z™: Hands-On Python & R In Data Science , this course...

In this post, I will collect all the useful resources to do Machine Learning, enter the Kaggle world and get good predictions !! Data Preprocessing  : # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv( 'Data.csv' ) X = dataset.iloc[:, :- 1 ].values y = dataset.iloc[:, 3 ].values # Splitting the dataset into the Training set and Test set from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2 , random_state= 0 ) # Feature Scaling from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) # Encoding categorical data from sklearn.preprocessing import LabelEncoder, OneHotEncoder labelencoder_X = LabelEncoder() X[:, 0 ] = labelencoder_X.fit_transform(X[:, 0 ]) onehotencoder = OneHotEncoder(categori...

Machine learning is the future, the more I read about it the more I get fascinated by this field. There are 2 YouTube channels I am currently following and I really encourage people who are interested in machine learning to follow them too, the two channels are  Two Minute Papers and  Siraj Raval , they always bring new subjects and they will make you interested in ML more than ever, even if you hate math x). So for a while I've been trying to learn more about ML but I really couldn't find time, I did some slow progress but there are a lot more I need to learn, this year (2018) advancing in ML gonna be one of my goals so I hope from now on I am gonna do more projects in ML and write more posts about it. In this post I am going to talk about the project I made the first semester, I didn't want to do a classic project so I talked to the prof and asked him to let me do something in ML and he liked the idea, as long as I use python (the subject course) he is Ok with it. ...

Recently I was following a machine learning Tutorial and even that ML requires a strong background in math, I am still fascinated with this domain and I want to advance more in this field. Yesterday I was following the part of Deep learning and how to make an Artificial Neural Networks (ANN) or Convolutional Neural Networks (CNN) and in one word deep learning is awesome! It was the first time I was up late trying to finish this tutorial part. Today, I implemented the CNN and I spent the whole day trying to make TensorFlow work on the GPU. What I did was writing a basic script, I made a model that trains using images of cats and dogs and afterward in the test set it will tell you if the image has a cat or a dog. The machine learning model is a classification between dogs and cats, this could be used with any other images like birds or cars or anything I just have to change the data set. I followed a tutorial, it's not something I did on my own and I am not going...

Hi, Today I was using Keras library with Tensorflow back end, I installed Tensorflow using this  documentation  (don't follow it the correct method is bellow) and I made sure to install the GPU version but when I was running my code it seemed that Tensorflow is using the CPU. After few minutes of Googling things up, I find a way to see if Tensorflow is using the CPU or the GPU by running this code in python : import tensorflow as tf hello = tf.constant( 'Hello, TensorFlow!' ) sess = tf.Session() print (sess.run(hello)) In my case I got an error : tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation 'MatMul_1': Operation was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/cpu:0 ]. Make sure the device specification refers to a valid device.  Like the error shows : the GPU wasn't detected, the reason is that my graphics card driver (NVIDIA) wasn'...