Orhan G. Yalçın
I help people learn Machine Learning and AI | Top writer in Artificial Intelligence | orhangaziyalcin.com | Let’s Connect: linkedin.com/in/OrhanGaziYalcin

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The guide to help you navigate around my content with ease.

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orhangaziyalcin.com

As you might know, I regularly write on Medium covering topics such as Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Data Visualization, TensorFlow, and other programming topics. Since the volume of my content reached a certain level, it got harder to see what I wrote about. So, I put together this guide to help you navigate around my content with ease.

I have been publishing in Towards Data Science for a long time and recently started publishing in The Startup. I publish my posts under the following series:

  • Deep Learning with TensorFlow 2.0
  • Deep Learning Case Studies
  • Kaggle’s Titanic Competition…


Using Convolutional Neural Networks to Classify Handwritten Digits with TensorFlow and Keras | Supervised Deep Learning

If you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let’s connect via Linkedin! Please do not hesitate to send a contact request! Orhan G. Yalçın - Linkedin

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MNIST Dataset and Number Classification by Katakoda

Before diving into this article, I just want to let you know that if you are into deep learning, I believe you should also check my other articles such as:

1 — Image Noise Reduction in 10 Minutes with Deep Convolutional Autoencoders where we learned to build autoencoders for image denoising;

2 — Predict Tomorrow’s Bitcoin (BTC) Price with Recurrent Neural Networks where we use an RNN to predict BTC prices and since it uses an API, the results always remain up-to-date.


← PART I | MACHINE LEARNING TUTORIALS | PART III → Soon

Improving Our Code to Obtain Better Results for Kaggle’s Titanic Competition with Data Analysis & Visualization and Gradient Boosting Algorithm

In Part-I of this tutorial, we developed a small python program with less than 20 lines that allowed us to enter the first Kaggle competition.

However, this model did not perform very well since we did not make good data exploration and preparation to understand the data and structure the model better. In Part-II of the tutorial, we will explore the dataset using Seaborn and Matplotlib. Besides, new concepts will be introduced and applied for a better performing model. Finally, we will increase our ranking in the second submission.

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Figure 1. Sea Trials of RMS Titanic on Wikipedia

Using Jupyter or Google Colab Notebook

For your programming environment, you may choose one of these two options: Jupyter Notebook and Google Colab…

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