Artificial intelligence is penetrating and transforming all walks of life at an unprecedented speed. One of the forces accelerating this change is deep learning. McKinsey research has published 12 areas where deep learning will impact, each divided into 10 areas. In other words, 120 business opportunities for deep learning!

The 12 industries that automation will take over in the short term, the darker the color, the greater the impact.

 

Take a look at overall industry trends in deep learning. Enterprise, we know the BAT, 360, sogou, drops are in deep learning ways such as layout, at the same time, domestic also emerged a group of new enterprises rely on deep learning, such as spirit deep pupil (security), automatic driving, university science and technology (face recognition), Thomson technology (face recognition), the horizon robot (ADAS), etc.

 

In terms of salary, the “White Paper on Internet Talent Trends 2017” released by BOSS Zhipin on the afternoon of January 10 shows that the average recruitment salary in the Internet industry in 2017 reached 10,600 yuan, up 3.1% year on year. Emerging technology positions led the way in terms of salary increases, with ai-related positions such as image algorithms, recommendation algorithms and deep learning all seeing salary increases of more than 15%.

 

Artificial intelligence is on the rise, but how do you quickly grow from a zero-based geek with a rough algorithm to a deep-learning engineer certified by Google, Facebook and other Silicon Valley companies?

 

Sebastian Thrun, GANs founder Ian Goodfellow, Google ****Deepmind scientist Andrew Trask and other top experts at Udacity, Launch “Deep Learning Cornerstone Nanodegree Program” **! After graduation, you will also receive a Google Technology certification and become the most sought-after deep learning engineer in the era of artificial intelligence and big data.

 

When you graduate, you’ll be ready to work in applications like ARTIFICIAL intelligence and driverless driving, with a 100% guarantee of admission to one of Udacity’s “Driverless Car Engineer Nanodegree” and “Artificial Intelligence Engineer NanoDegree.” Upon graduation from these programs, you’ll also receive Udacity’s job referral service. Have the opportunity to join ****IBM, Mercedes Benz, Nvidia, Didi Chuxing, BMW, Uber and other leading technology enterprises! * * * * * * * *

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Over the next 21 weeks, not only will you be mentored by silicon Valley’s top instructors, but you will also be challenged by the cool practical project \

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Prior knowledge ********

Deep Learning (semester 1) is an entry level course with zero foundation!

 

Python syntax and data structures ****

In this section, you’ll learn about Python’s integer and string data types, learn how to store data using variables, and learn how to use built-in functions and methods. You’ll learn about conditional statements, loop statements and complex statistics. You will also learn to use collection data types, including lists, collections, and dictionaries.

 

Actual project: Analyzing phone calls and text messages

Python files and networks ****

 

You’ll get to know Python’s powerful libraries by using modules from both the Python standard library and third-party libraries. And learn to read data from files on disk and use online resources to solve practical problems. Finally you will practice writing a web crawler to track links between Wikipedia articles.

 

Actual combat project: Explore the data of shared bikes in the United States

Introduction to data analysis ********

 

Learn how to handle multiple data sets in Python and Pandas. Using Python, Numpy, and Pandas, you will learn how to clean, explore, analyze, and visualize data.

 

Field project: Exploring data sets

Fundamentals of linear algebra ********

 

Linear algebra is the foundation of deep neural networks. In this section, you will learn about vectors and intersections in linear algebra and the basic operations to implement vectors starting from zero.

 

Practical project: linear algebra

Evaluation and validation of the model ********

 

The evaluation index of the model is a very important part in the process of deep learning modeling. In this section, you will learn how to weigh deep learning models against other machine learning models.

 

Actual combat project: forecast future housing prices

To begin the second semester of the program, you will need to have a basic knowledge of Python and be able to use Numpy and Pandas. You also need to have some knowledge of algebra, multivariable calculus, and linear algebra.

Introduction to deep learning ********

 

Learn what you will learn in this course and explore the application of deep learning networks in different areas. You will also take your first steps in deep learning through a series of short courses, learning to use deep learning-related tools such as Anaconda and Jupyter notebooks.

Neural network ********

 

Neural networks are the cornerstone of deep learning. In this part of the course, you will learn the basic principles of neural networks and build a neural network from scratch using Python and Numpy in a real-world project. You’ll also learn briefly about TensorFlow and how to use it to build deep neural networks.

 

Actual project: Your first neural network

Convolutional neural networks ********

 

Convolutional neural networks are the standard solution to visual problems. It has applications in driverless cars, facial recognition, medical imaging, and so on. In this part of the course, you will learn the basic principles of convolutional neural networks and use them in practical projects to solve image classification problems.

 

Actual combat project: dog breed identification

Recurrent neural network ********

 

Build your own recurrent neural network (RNN) and Long and short term memory neural network (LSTM) with Keras and TensorFlow, and apply them to the frontiers of text sentiment analysis and text generation. Challenge “generating drama script” actual combat project.

 

Actual combat project: generate drama script

Generate an adversarial network ********

 

Follow Ian Goodfellow, the father of generative adversarial network, to learn and master the deep convolutional generative adversarial network (DCGAN) model to simulate the generation of real images.

 

Actual combat project: generate human face

Deep reinforcement learning ********

 

Deep neural networks are used to design a system that can make decisions in a simulated environment. Apply reinforcement learning to complex fields such as video games and robot development.

 

Actual combat project: training quadcopter learn to fly

Along the way, you can enjoy Udacity’s mentorship, line-by-line code review, synchronous learning groups and more. Put in 10 hours a week and you’ll be a Silicon Valley-certified deep learning expert by the end of 21 weeks. \

 

Registration time for this deep Learning course:

From January 18th to February 7th

Limited to 200 seats in China!

Scan the qr code below to join the group to grab seats

You will also receive $300 in red envelopes

Time is limited

Join in!