What knowledge do you need to know to enter the field of deep learning?

First of all, you must learn to program, especially python programming.

Then there are the deep learning frameworks, which are currently represented by several deep learning frameworks, such as TensorFlow and PyTorch.

It is recommended to have some understanding of these two frameworks and then choose one to go deeper.

Of course, you can also learn FastAI directly, if you want to do research, then you need to study a lot of mathematical theory.

There are many people who love to learn, but not many people who can learn. The education we received from childhood taught us that learning is more about memorizing knowledge points and being able to solve exercises, and no one has ever taught us how to study effectively or learn deeply. Deep learning is inherently a form of competitiveness.

The learning process is the intrinsic rules and representation levels of the sample data, and the information obtained during the learning process is of great help to the interpretation of data such as words, images, and sounds. The ultimate goal is for machines to be able to learn analytically like humans, and to recognize data such as text, images, and sounds. Deep learning is a sophisticated machine learning algorithm that achieves far more performance in speech and image recognition than previous technologies.

Deep learning has achieved a lot in search technology, data mining, machine learning, machine translation, natural language processing, multi** learning, speech, recommendation and personalization techniques, and other related fields. Deep learning enables machines to imitate human activities such as audio-visual and thinking, solves many complex pattern recognition problems, and makes great progress in artificial intelligence-related technologies.

Background. Machine learning is a discipline that studies how computers simulate or implement human learning behaviors in order to acquire new knowledge or skills, and to reorganize existing knowledge structures to continuously improve their performance.

In 1959, Samuel in the United States designed a chess program that had the ability to learn and improve his chess skills through continuous play. 4 years later, this program prevailed over the designers themselves.

After another 3 years, the program defeated an undefeated champion in the United States who had been winning for 8 years. This program shows people the power of machine learning and asks many thought-provoking social and philosophical questions.

Taking the excellent employment in-depth course as an example, the main course content of deep learning includes the following stages: AI overview and introduction of cutting-edge application achievements, artificial neural network and convolutional neural network principles and Tensorflow practice, recurrent neural network principles and project practice, generative adversarial network principles and project practice, deep learning distributed processing and project practice, deep reinforcement learning and project practice, enterprise-level project practice - license plate recognition project practice, Introduction to the latest cutting-edge technologies of deep learningEight stages, these are the content to be learned in deep learning.

Deep learning is a general term for a class of pattern analysis methods, and in terms of specific research content, it mainly involves three types of methods:

1) A neural network system based on convolutional operations, i.e., convolutional neural networks (CNNs).

2) Autoencoder neural networks based on multilayer neurons, including auto encoder and sparse coding, which have received extensive attention in recent years.

3) The Deep Belief Network (DBN) is pre-trained in the form of multi-layer autoencoder neural network, and then combined with the discrimination information to further optimize the neural network weight.

Preparatory knowledge of deep learning: mathematical fundamentals (linear algebra, matrices, probability statistics, optimization, etc.), machine learning fundamentals, programming fundamentals; neural networks, deep network structures, image tasks, speech tasks, natural language tasks; How to use the deep learning framework to complete the construction and training of the network.

If you want to learn.

For BAIn deep learning, you first need to have du

Programming foundation, if you don't have a DAO programming foundation, then DAO is relatively difficult.

Taking the excellent employment deep learning course as an example, the course also learns the principles of recurrent neural networks, artificial neural networks and convolutional neural networks, generative adversarial networks, etc., and also includes actual project combat, if you are interested, you can learn about it.

Fundamentals of mathematics and programming are both important.

Advanced mathematics, linear algebra, probability theory and mathematical statistics, these are all to be mastered.

To learn deep learning, you need to have a python programming foundation. In the field of deep learning, Python is regarded as the most concise and straightforward scripting language, and is widely used in scientific research and engineering. If you have a foundation in Python, it will be easier to learn, but the subsequent courses will also be difficult, and you need to learn it seriously.

At least 2 years of programming experience, preferably basic knowledge of machine learning in Python language, especially deep learning.

Basic knowledge of weighted computing, understanding linear algebra such as mean, variance, standard deviation, etc., understanding vectors, matrices, etc.

Calculus, understanding differentiation, integration, partial derivatives, etc.

Zhonggong Education has a new online course on deep learning, you can pay attention to it.

1.Linear classifier score function.

2. Linear classifier comprehension --- spatial division.

3. lossfunction costfunction -- measure the degree of matching.

4.Optimization and gradient descent.

Advanced mathematics, linear algebra, probability theory and mathematical statistics, etc., I heard that the Chinese public and the Chinese Academy of Sciences have launched deep learning courses.

If you want to take the direction of deep learning in the future, in fact, the courses of the undergraduate mathematics department are basically enough, if it is not enough, you can check and fill in the gaps and read some materials and books. There is no need to go for a postgraduate degree in mathematics.

I recommend the book "Deep Learning". The authors are Ian Goodfellow, Yoshua Bengio and Aaron Courville. The Chinese version of the book was released on July 22, 2017.

The book was produced by a number of translators. The book "Deep Learning" introduces the basic mathematical knowledge, machine learning experience, and the theory and development of deep learning at this stage from a shallow to a deep level. In addition, if readers want to be familiar with some mathematical knowledge, this book also makes some introductions, including basic content such as matrices and derivatives.

Readers can read from beginning to end.

One of the major features of the book "Deep Learning" is that it introduces the essence of deep learning algorithms, which is separated from the specific ** implementation and gives the logic behind the algorithm, and people who don't write ** can also read it. In order to facilitate the reader's reading, the author has specially drawn an organizational chart of the content of the book, and pointed out the correlation between the contents of the 20 chapters of the book. Readers can choose and read according to their background or needs.

Deep learning. The content of the book is divided into 3 parts: the first part introduces the background of deep learning, provides preparatory knowledge, and includes the concepts and techniques of deep learning; The second part describes the important components of deep learning computing, and also explains the convolutional neural networks and recurrent neural networks that have made deep learning successful in several fields in recent years. The third part evaluates the optimization algorithm, examines the important factors affecting the computing performance of deep learning, and lists the important applications of deep learning in computer vision and natural language processing.

Get hands-on with deep learning.

JD.com If you feel that your mathematical knowledge is not enough, you can read this book "The Mathematics of Deep Learning". Based on a wealth of illustrations and concrete examples, this book provides an easy-to-understand introduction to mathematics related to deep learning. Chapter 1 provides an overview of neural networks; Chapter 2 introduces the mathematical fundamentals needed to understand neural networks; Chapter 3 introduces the optimization of neural networks; Chapter 4 introduces neural networks and error backpropagation methods; Chapter 5 introduces deep learning and convolutional neural networks.

The book uses Excel for theoretical verification to help readers intuitively experience the principles of deep learning.

It is recommended that you learn technology, now technical talents are very popular, and the state is paying more and more attention to the training of technical personnel. For example, car maintenance technology has good job prospects and high salaries.

The depth of study varies depending on the major, and you can consult others according to your major.

What do you need to learn in Xindu Learning? There are many, many things that you don't learn, so it depends on which ones you learn.

Deep learning is one of the means to implement artificial intelligence, which is a method of machine learning that attempts to abstract data at a high level using algorithms that contain complex structures or multiple processing layers (neural networks) composed of multiple nonlinear transformations. Deep learning can be understood as the development of neural networks, which abstract and model the basic characteristics of the human brain or biological neural networks, which can learn from the external environment and adapt to the environment in a similar way to interacting with living beings.

The main course content of deep learning includes the following stages: AI overview and introduction to cutting-edge application results, artificial neural network and convolutional neural network principles and Tensorflow practice, recurrent neural network principles and project practice, generative adversarial network principles and project practice, deep learning distributed processing and project practice, deep reinforcement learning and project practice, enterprise-level project practice - license plate recognition project practice, deep learning latest frontier technology introduction to eight stages. That's what deep learning is all about.

This course is still very promising because there is a large talent gap in this area, and this course covers 75% of the technical points in the industry to meet all kinds of employment needs. Moreover, the relevant institutions of the Institute of Automation of the Chinese Academy of Sciences will issue certificates and give away the source code of the enterprise-level projects in the course. Therefore, there is definitely no problem in applying for a job, and there are also assistance blessings such as certificate source code after learning, and the future is very bright.

The essence of deep learning is to learn more useful features by building machine learning models with many hidden layers and massive training data, so as to ultimately improve the accuracy of classification or **. Therefore, "deep model" is the means, and "feature learning" is the end.

Different from traditional shallow learning, deep learning differs in that: 1) it emphasizes the depth of the model structure, usually with 5, 6, or even more than 10 layers of hidden layer nodes; 2) The importance of feature learning is clearly highlighted, that is, the feature representation of the sample in the original space is transformed into a new feature space through layer-by-layer feature transformation, so as to make classification or ** easier. Compared with the method of constructing features by artificial rules, the use of big data to learn features can better describe the rich internal information of data.

Deep learning is to learn a lot of content, for example, mathematics will learn mathematical units of operation or something.

There are many positions that can be pursued.

Such as artificial intelligence algorithm engineers, deep learning algorithm engineers, computer vision engineers, natural language processing algorithm engineers, deep learning training engineers, image processing algorithm engineers, intelligent manufacturing algorithm engineers, reinforcement learning engineers, etc.

Learn deep learning, and the employment prospects are very broad.

In his new book "Deep Learning", Terrence Schenofsky, chairman of the NIPS (Neural Information Processing Systems Conference) group, has painted a picture of the future for us, allowing us to understand and meet the trends.

AI Healthcare: More Accurate Diagnosis and **.

Deep learning based on big data will transform the healthcare industry, providing faster and more accurate diagnosis of diseases and**.

Education for the Future: Become a better learner.

Traditional schooling teaches children too much information, too many pre-existing skills, and technological developments can quickly make these things obsolete. In the future, artificial intelligence will change the status quo of traditional education.

Social Transformation: The Rise of Social Robots.

The development of artificial intelligence has changed the way we socialize, and even the objects we socialize.

Cross-cultural communication: speech recognition and language translation.

Deep learning has brought speech recognition and language translation together, and cross-border and even cross-cultural communication involving multiple languages will no longer be a problem.

The Future of Identity Collapse: Facial Recognition and Biometric Scanning.

In the future, your identification may not be an ID card or a passport, but your face. Biometric scanning will be an important development direction for personal identification in the future.