The world’s leading publication for data science, AI, and ML professionals.

November Edition: The Power of Deep Learning

Building biologically inspired algorithms

Monthly Edition

Photo by Ryoji Iwata on Unsplash
Photo by Ryoji Iwata on Unsplash

When I was just getting started in Data Science, I never thought I would need to tackle deep learning. Deep learning isn’t a concept for newbies! I naively thought that I could avoid it. Suddenly, just months into my journey, I was trying to optimize a task by building a recommendation system and needed to understand which NLP model would fit my problem and why. It was time to face the music.

As I began to dive into the topic, I realized that building a deep learning model is a lot like putting a jigsaw puzzle together without looking at the picture on the box. First, we pick out the pieces that we identify as the edges due to the shape. Next, we separate the rest of the puzzle pieces by color. Based on the colors of the edge pieces, we might be able to guess the approximate location of each color group. After that, we pick out the pieces within a subgroup of colors which we can easily identify are related and start to put them together. Piece by piece, we realize it’s Van Gogh’s The Night Café.

Like this analogy, a Deep Learning model is a masterpiece composed of many simpler concepts and functions. This idea is what makes deep learning such a powerful and widely used technique. Whether you are a newbie, a PhD in the tech field or anyone who cares about society, you will run into a deep learning topic sooner than you think. To help you learn more, we’ve put together some of our favorite picks on this topic, from introductory ideas to the state-of-art techniques. We hope they will shed new light on your current understanding and help you navigate this journey.

Linda Chen, Editor at Towards Data Science.


The Roadmap of Mathematics for Deep Learning

Understanding the inner workings of neural networks from the ground-up

by Tivadar Danka – 19 min read


Deep Learning with CIFAR-10

Image Classification using CNN

by Aarya Brahmane – 10 min read


Solve real-world problems using Deep Learning & Artificial Intelligence

Not your usual object detection blog

By Vaishnavi Dwivedi – 6 min read


Do you Really Need A GPU For Deep Learning?

Is acquiring a GPU an essential requirement for deep learning? Understanding GPU, its benefits, and exploring alternatives

By Bharath K – 7 min read


My 3-year journey: From zero Python to Deep Learning competition master

The path I followed since starting to learn Python back in 2017 up to became a Kaggle competition master with a solo gold medal in middle 2019. A story to inspire you to pursue your goals and never give up.

By Miguel Pinto – 6 min read


Deep learning on graphs: successes, challenges, and next steps

This is the first in a series of posts where I will discuss the evolution and future trends in the field of deep learning on graphs.

By Michael Bronstein – 7 min read


Deep learning isn’t hard anymore

At least, building software with deep learning isn’t

By Caleb Kaiser – 6 min read


MacBook Pro for Deep Learning? Let’s Try.

How will it compare against Nvidia GPU powered laptop?

By Dario Radečić – 4 min read


New podcasts


We also thank all the great new writers who joined us recently Federico Urena, Jason Yonglin Wu, Vaishnavi Dwivedi, Nicholas Lewis, Anjali Bhardwaj, Chris McCormick, Amir Afianian, Thouheed Gaffoor, Yingyuan (Valerie) Zhang, Diane Tunnicliffe, Nikhil Bhandari, Patrick Da Silva, Saurav Singla, Timo Bohnstedt, Alexandros Korkovelos, Vicky Yu, Das Wijesundera, Tyler Harris, Gabriel Luciano Pietrafesa, Irene P, Alireza Koochali, Mihail-Iulian Pleșa, Daniele Micci-Barreca, and many others. We invite you to take a look at their profiles and check out their work.


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