A complete guide to Deep learning for Python

Author: Chen_H wechat & QQ: 862251340 wechat official account: Coderpai My blog: please click here

• Original: www.analyticsvidhya.com/blog/2016/0…

  introduce

  Deep learning has become a prominent topic in the field of artificial intelligence. It is known for excelling in areas such as “computer vision” and gaming (AlphaGo), even beyond human ability. In recent years, attention to deep learning has also been increasing, and here is a survey result for reference.

  Here’s a Google search trend chart:

  

  If you are interested in this topic, here is a good non-technical introduction. If you’re interested in seeing the latest trends, here’s a good round-up.

  In this article, our goal is to provide a learning path for all deep learners, as well as a path to explore for those who want to learn further. If you’re ready, let’s get started!

  Step 0: Prerequisites

  It is recommended that before you start learning deep learning, you should first understand some basic knowledge of machine learning. This article lists complete resources for learning machine learning.

  If you want an easy learning version. Look at the following list:

  • Basic Mathematics (especially calculus, probability, and linear algebra)

  • Python based

  • Fundamentals of statistics

  • Fundamentals of machine learning

Recommended time: 2-6 months

Step 1: Machine configuration

Before proceeding to the next step, you should make sure that you have a hardware environment that supports your learning. It is generally recommended that you have at least the following hardware:

• A good enough GPU (4+ GB), preferably Nvidia

• An acceptable CPU (e.g., Intel Core I3, Intel Pentium may not be suitable)

• 4 GB RAM (this depends on the data set size)

If you’re not sure, read this hardware guide.

If you don’t have the required specs, you can rent a cloud platform to learn from, such as Amazon Web Services (AWS). This is a good guide to deep learning using AWS.

Note: Do not install any deep learning libraries at this stage, we will cover the installation process in Step 3.

Step 2: Try deep learning for the first time

Now that you have an overview of the field, you should dig deeper into deep learning.

According to our own preferences, we can choose the following ways:

• Learn from blogs, like Fundamentals of Deep Learning, and Hacker’s Guide to Neural Networks.

• Learn via video, such as Deep Learning Simplified.

• Learn from books, such as Neural networks and Deep Learning

In addition to the basics above, you should also be familiar with some popular deep learning libraries and the languages that run them. Here’s a less complete list (you can check the wiki for a more complete list) :

• Caffe

• DeepLearning4j

• Tensorflow

• Theano

• Torch

Some other notable libraries: Mocha, Neon, H2O, MXNet, Keras, Lasagne, Nolearn. For deep learning languages, check out this article.

You can also check out Lecture 12 of Stanford’s CS231n for an overview of some of the deep learning libraries.

Recommended duration: 1-3 weeks

Step 3: Choose your own field

This is the most interesting part. Deep learning has been applied in various fields and has achieved the most advanced research results. If you want to learn more, the best path for you as a reader is to get your hands on it. This will give you a deeper understanding of what you know now.

Note: In each of the following areas, there will be a blog, a field project, a required deep learning library, and a supporting course. The first step is to learn about blogging, then install the corresponding deep learning library, and then do the actual project. If you run into any problems along the way, you can take a supporting course.

• Application of deep learning in machine vision

  • See blog DL for Computer Vision

  • Actual combat project: Facial Keypoint Detection

  • Deep learning library: Nolearn

  • Convolutional Neural Networks for Visual Recognition

• Deep learning in natural language processing

  • See blogs: Deep Learning, NLP, and Representations

  • Deep Learning for Chatbots, Part 1, Part2.

  • Deep learning library: Tensorflow

  • CS224d: Deep Learning for Natural Language Processing

• Application of deep learning in speech

  • Deep Speech: Lessons from Deep Learning

  • Actual project: Music Generation using Magenta (Tensorflow)

  • Deep learning library: Magenta

  • Recommended courses: Deep Learning (Spring 2016), CILVR Lab@NYU

• Application of deep learning in reinforcement learning

  • Deep Reinforcement Learning: Pong from Pixels

  • Deep Learning Libraries: There are no deep learning libraries required, but you will need openAI Gym to test your models.

  • CS294: Deep Reinforcement Learning

Recommended time: 1-2 months

Step 4: Dig deep into deep learning

By now you should have learned the basics of deep learning algorithms! But the journey ahead will be much harder. Now, you can use this newly acquired skill as efficiently as possible. Here are some tips you should do to hone your skills.

• Repeat the steps above and choose different areas to try.

• Applications of deep learning in other fields. DL for trading, DL for Optimizing Energy Efficiency.

• Use the mind skills you’ve learned to do something else, like refer to this website.

• Enter competitions like Kaggle.

• Join deep learning communities like Google Group, DL Subreddit.

• Follow some researchers, such as re.Work DL Summit.

Suggested time: unlimited

——————-

Some good resources:

• Complete Deep Learning book

• Stanford UFLDL Turorial

• Deep Learning in Neural Networks: An Overview

• Awesome Deep Learning github repository

• Yann LeCun’s recommendations for Deep Learning self-study

CoderPai is a platform that focuses on algorithms in action, from basic algorithms to artificial intelligence algorithms. If you are interested in algorithms in action, please follow us. Join AI combat wechat group, AI combat QQ group, ACM algorithm wechat group, ACM algorithm QQ group. For details, please pay attention to the wechat account CoderPai.