Neural networks

Welcome to the presentation on neural networks! Neural networks are an amazing artificial intelligence technology capable of processing information and making human-like decisions. They are widely used in medicine, finance, transport and technology. Together, we explore how neural networks work, their potential, and the challenges they face. Are you ready? Let's start the presentation! //1  слайд, тупо название темы и определение нейросетей(2 предложние),миша говорит весь этот текст

On this slide, I will tell you about how neural networks function. Neural networks take input and process it through layers of neurons that perform various operations such as linear transformations and activation functions. Each neuron passes its result to the next layer, allowing the neural network to extract more complex and abstract features of the data. Neural networks are trained by adjusting the weights and network parameters based on the backpropagation of the error. This allows the neural network to improve its ability to classify, predict, and make decisions as new data becomes available.  // 2  слайд, тут нужно тупо рассказать как они работают, текст весь вставлять нельзя, так что тупо вставить умные картинки без текста, или мемчик засунуть и сказать типо весь текст не поместился на этом слайде, поэтому я его зачитаю а вы пока посмотрите на этого милого кролика

Different types of neural networks:

On this slide, I would like to tell you about the different types of neural networks. There are many types of neural networks, each of which specializes in certain types of tasks. One of the most common types is the Convolutional Neural Network (CNN), which is widely used for image processing and analysis. The Recurrent Neural Network (RNN) is particularly well suited for dealing with sequential data such as text or time series. Deep Neural Networks (DNNs) consist of many layers and can be processed // можно прочитать вместе со 2 слайдом либо просто перечисли типы нейросетей на презе

Brief history overview:

In this slide, I will briefly present the history of the development of neural networks. The study of neural networks began in the 1940s, when scientists tried to create models that could mimic the workings of the human brаin. However, progress has been slow due to limited computing power and lack of data. In the 1980s, there wаs а significant breakthrough with the development of the backpropagation algorithm, which became the bаsis for learning neural networks. In the following decades, neural networks became more and more popular, and todаy we can observe their wi2de application in various fields.

//на слайде надо красиво представить события проходящие за это время(можно тупо в столбик можно типо шкалой времени

1943: McCulloch-Pitts neuron model

1956: Dartmouth College Conference and the birth of the concept of "artificial intelligence"

1969: Emergence of Rosenblatt's perceptron

1986: Development of an error backpropagation algorithm

1997: Computer Deep Blue wins over chess champion Garry Kasparov

2012: The victory of the neural network in the ImageNet competition

2014: Revival of neural networks and development of deep neural networks

2018: Development of generative adversarial networks (GAN)

2020: Application of neural networks in the field of autonomous vehicles

*важные события

On this slide, we will consider the prospects for the further development of neural networks. There are many areas where neural networks can play a key role and make a significant contribution. For example, in medicine, they can help in the diagnosis and treatment of diseases, in autonomous systems - in the development of autonomous cars and robots, and in the financial sector - in predicting market trends and optimizing investments. Neural networks can also be applied in the field of artificial intelligence, games, speech recognition, natural language, and many others.

//На слайде презентации можно представить чет из этого:

Заголовок "Перспективы развития нейросетей".

Иконки, символизирующие различные области применения нейросетей, такие как медицина, автономные системы, финансы, искусственный интеллект и другие.

Краткий список примеров конкретных задач и проблем, которые могут быть решены с помощью нейросетей.

Иллюстрации, показывающие потенциальные преимущества и возможности, которые нейросети могут принести в будущем.

Важные ключевые слова и фразы, подчеркивающие значимость и перспективность нейросетей в современном мире.

On this slide, we will give specific examples of the use of neuraI networks in various areas:

Medicine: Neural networks can help diagnose diseases, analize medical images, develop personalized medicine, and predict epidemics.

Finance: Neural networks can be used to predict financial markets, detect fraud, manage portfolios, and automate trading strategies.

Manufacturing: Neural networks can help optimize manufacturing processes, quality control, equipment failure prediction, and energy optimization.

Transportation: Neural networks can be applied to autonomous vehicles, routing, urban traffic optimization, and traffic flow prediction.

Nаtural language: Neural networks can be used for naturaI Ianguage processing and analysis, automatic translation, text generation, and content recommendation.

//тут для каждого подпункта будет свой слай где будут картинки с применением

Overview of teaching methods:

On this slide, we will present an overview of various neural network training methods that play an important role in achieving high performance and efficiency of neural networks. Presenting