Diving into Deep Learning

3. History of Deep Learning, from Biology to Programming 3.1. How did everything started? I am not going to include here a deep explanation(redundancies apart (; ) of the history of Deep Learning; but if you are interested, some useful resources I have found are: HISTORY OF DEEP LEARNING - Github.io HISTORY OF DEEP LEARNING - Import.io HISTORY OF DEEP LEARNING - Dataversity.net HISTORY OF DEEP LEARNING - Forbes However, I am going to explain the evolution from the Biological origin (sorry I am a Biomedical Engineering, I had to include my “bio-” prefix somewhere!) 3.2. Biological Neural Networks KIYOSHI KAWAGUCHI - Biological Neural Networks ALBRECHT SCHMIDT - Biological Neural Networks SOPHOS - Artificial and Biological NN 3.3. The Basis of Biological Neural Networks: The Perceptron The psychologist Frank Rosenblatt’s  conceived the Perceptron from the idea of a neuron. It was defined as a simplified mathematical model of how the neuro

2. Our environment in Anaconda We will use this free and open source , with applications related to: large-scale data processing, predictive analytics , scientific computing ; that aims to simplify package management and deployment. Ref 2.1. How to install Anaconda Easy ! Follow my steps: 1. Go to http://www.anaconda.org 2. Slide to the bottom of the page 3. Click on “Download Anaconda” 4. A new tab will open (see below), where you decide if you want Python 2.7 or 3.6 I recommend the version 3.6 5. Click on the chosen version and follow the downloading process (read the conditions, accept them, select where to store the Navigator…) 6. I recommend installing it inside the User Directory (in case you are sharing the computer with other Users), for instance in the Desktop All set! 2.2. How to create our environment If installing Anaconda was easy, this is a piece of cake! Follow my steps: 1. Open Anaconda Navigator (I’d keep it in Dock so

  1. Definition 1.1. What is Deep Learning?   “Deep learning is the state-of-the-art approach across many domains, including object recognition and identification, text understating and translation, question answering, and more. In addition, it is expected to play a key role in many new usages deemed almost impossible before, such as fully autonomous driving”, Ronny Ronen It looks like a lot of fun, right? I am sure you already know what you need Deep Learning for, which I bet is something fascinating. So without making it longer… Let’s start diving!! I will try to make this manual as concise and self-explanatory as possible. I still lack of experience so I apologize in advance if there is anything that is not clear or does not work. If you let me know I’ll do my best to improve the explanation or fix the problems as soon as I can. By the way, in this handeable manual we can find a glossary of terms in the end. I will try to set in italics (or other marking?) everytime a word a