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In June 2018, OpenAI presented the GPT neural network model, which immediately showed the best results in a number of language tests. GDP-2 appeared in 2019, and GPT-3 was presented in May 2020. These models demonstrated the ability of the neural network to generate related text. Additional experiments concerned the ability to generate music and images. The main disadvantage of such models is connected with the computing resources they involve. It took a month to train the first GPT on a machine with 8 GPUs. This disadvantage can be partially compensated by the possibility of using pre-trained models to solve new problems. But considerable resources are required to maintain the model functioning considering its size.

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1. Neural networks made easy - MT5
2. Neural networks made easy (Part 2): Network training and testing - MT5
3. Neural networks made easy (Part 3): Convolutional networks - MT5
4. Neural networks made easy (Part 4): Recurrent networks - MT5
5. Neural networks made easy (Part 5): Multithreaded calculations in OpenCL - MT5
6. Neural networks made easy (Part 6): Experimenting with the neural network learning rate - MT5
7. Neural networks made easy (Part 7): Adaptive optimization methods - MT5
8. Neural networks made easy (Part 8): Attention mechanisms - MT5
9. Neural networks made easy (Part 9): Documenting the work - MT5
10. Neural networks made easy (Part 10): Multi-Head Attention - MT5
11. Neural networks made easy (Part 11): A take on GPT - MT5
12. Neural networks made easy (Part 12): Dropout - MT5
13. Neural networks made easy (Part 13): Batch Normalization - MT5

Table of Contents

• Introduction
• Partial position closing algorithm, utilizing last bar movement
• Hybrid lot variation algorithm
• Averaging principle
• Direct and indirect signs of the consistency of a trading technique
• Using balance and equity waves
• Conclusion

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We have been working hard studying various approaches to using machine learning aimed at finding patterns in the forex market. You already know how to train models and implement them. But there are a large number of approaches to trading, almost every one of which can be improved by applying modern machine learning algorithms. One of the most popular algorithms is the grid and/or martingale. Before writing this article, I did a little exploratory analysis, searching for the relevant information on the Internet. Surprisingly, this approach has little to no coverage in the global network. I had a little survey among the community members regarding the prospects of such a solution, and the majority answered that they did not even know how to approach this topic, but the idea itself sounded interesting. Although, the idea itself seems quite simple.

Let us conduct a series of experiments with two purposes. First, we will try to prove that this is not as difficult as it might seem at first glance. Second, we will try to find out if this approach is applicable and effective.

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Contents

• Introduction
• 1. Dropout: A Method for Increasing Neural Network Convergence
• 2. Implementation
  
  • 2.1. Creating a New Class for Our Model
  • 2.2. Feed-Forward
  • 2.3. Feed-Backward
  • 2.4. Class Saving and Loading Methods
  • 2.5. Changes in the Neural Network Base Classes
  
  
• 3. Test
• Conclusion
• References
• Programs Used in the Article

Since the beginning of this series of articles, we have already made a big progress in studying various neural network models. But the learning process was always performed without our participation. At the same time, there is always a desire to somehow help the neural network to improve training results, which can also be referred to as the convergence of the neural network. In this article we will consider one of such methods entitled Dropout.

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Contents

• Introduction
• 1. Theoretical Prerequisites for Normalization
• 2. Implementation
  
  • 2.1. Creating a New Class for Our Model
  • 2.2. Feed-Forward
  • 2.3. Feed-Backward
  • 2.4. Changes in the Neural Network Base Classes
  
  
• 3. Testing
• Conclusion
• References
• Programs Used in the Article

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In order for us to become Better Programmers we not only need to learn code 24/7 but also we need to learn about the best practices, if not to learn how to learn coding at all. Learning how to learn code along with the best practices to apply is very crucial to everyone who needs to have a successful coding career. By knowing the hows and whys, we become aware of what we are doing—this increases our ability to remember and helps us improve our code in general.

The following ways are the proven methods that can change your programming experience and they way you do it, almost overnight, if you attentively apply everything that you are about to read in this article.

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The first article is here for those who haven't read it, so let me continue this mission to turn noobs to professionals.

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Noobs are the most misunderstood coders, when It comes to coding world. Unpredictable is one of the biggest behaviors of a noob. You never know what they are coming up with inside their code, even if they tell you what they want to implement. Among all the reasons, the biggest reason is that they are Inconsistent in what they are doing.

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In this article we are going to discuss the broader term of developer — how you can become faster at developing your systems. We are going to study everything in between design, work ethics, tools to use, coding, debugging, etc.

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Contents

• Introduction
• How can probability theory be useful in market analysis?
• Specifics of applied probability theory for manual and automated trading
• Probability trees and hypotheses
• About fractals
• Bernoulli scheme
• Creating the first fractal
• Summary
• Conclusion

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