Твоя ПЕРВАЯ НЕЙРОСЕТЬ на Python с нуля! | За 10 минут :3

Хауди Хо™ - Просто о мире IT!

31 Jul 202318:30

Summary

TLDRIn this educational video, the host addresses previous criticisms by demonstrating how to build a neural network from scratch, in contrast to using frameworks like TensorFlow. The lesson delves into the fundamentals of neural network operations, explaining concepts like neurons, bias, and various activation functions such as rectifiers. The tutorial focuses on a practical project, teaching viewers to create a neural network in Python that can recognize handwritten digits using the MNIST dataset. The video promises to accomplish this in less than 100 lines of code, emphasizing ease of learning and implementation. Additionally, it covers topics like model saving, inference making, and packaging into a library. The host also recommends additional Python development courses for viewers interested in deepening their understanding.

Takeaways

😀 The tutorial aims to correct a previous lesson by showing how to build a neural network from scratch, introducing basic concepts and principles of neural networks.
🙂 It demonstrates the entire process of creating a neural network in Python, including writing less than 100 lines of code for the task.
🙌 The tutorial covers various foundational topics such as neuron bias, activation functions like the rectified linear unit (ReLU) and sigmoid, and the importance of these concepts.
📝 The practical example focuses on teaching the neural network to recognize handwritten digits using the public MNIST dataset, which contains 60,000 training images.
📱 Highlighting the neural network's architecture, the video explains the structure of neural networks including input, hidden, and output layers, and how they are connected.
📚 Discusses the process of initializing weights randomly as a common practice and explains its significance in the neural network's ability to learn.
📈 Offers insights on saving and inferring with a trained model, and even packaging it into a genuine neural network library.
🚀 Emphasizes the compactness and efficiency of neural networks by mentioning that the core part of the network can be encapsulated in just six lines of code.
👨‍💻 Introduces additional learning resources for Python and neural networks, encouraging further exploration and mastery of these technologies.
😃 Concludes with a successful demonstration of the neural network's ability to recognize a digit it hadn't encountered in the training set, underscoring the effectiveness of the teaching method.

Q & A

What was the main reason for creating a new lesson on neural networks?
-The main reason was to address feedback from a previous lesson that focused on working with TensorFlow and PyTorch, and instead explain how to write a neural network from scratch.
What is the goal of the lesson mentioned in the script?
-The goal is to fix the situation by introducing the basic principles of neural networks from scratch, including neurons, bias, and different activation functions, and to teach how to save a trained model and perform inference.
How many training images does the public dataset mentioned in the script contain?
-The public dataset contains 60,000 training images.
What is the complexity of the neural network code mentioned in the script?
-The neural network is written in less than 100 lines of code, with the most complex part fitting into just 6 lines.
What example is used in the script to illustrate how our brain recognizes handwritten digits?
-The example used is recognizing the handwritten digit '3', demonstrating how our brain's neural network can identify it despite variations in handwriting.
What libraries are recommended to install for following along with the lesson?
-It's recommended to install NumPy for mathematical operations and Matplotlib for data visualization.
What are the two matrices used in the neural network's simplified example?
-The first matrix connects the input layer to the hidden layer and has a shape of 4x5. The second matrix connects the hidden layer to the output layer and has a shape of 3x4.
Why is it important to initialize the weights of a neural network randomly?
-Random initialization of weights is important because it affects the neural network's ability to learn. If there was a way to initialize weights correctly from the beginning, there would be no need for training.
What is the role of a bias neuron in a neural network?
-A bias neuron is used to adjust the shift of the activation function graph to the left or right, helping to better fit the model.
How did the neural network perform in recognizing handwritten digits after training?
-After three epochs of training, the neural network learned to recognize handwritten digits with an accuracy of up to 93%.

Outlines

00:00

🧠 Introduction to Neural Networks

This segment addresses previous criticism by detailing how to create a neural network from scratch, explaining the basic principles of neural networks, neuron bias, and activation functions such as rectifiers and softmax. It proposes to teach neural networks through a hands-on approach, using Python to recognize handwritten digits with less than 100 lines of code. The challenge is simplified by focusing on a smaller portion of the neural network that consists of only six lines of code, making the learning process seem less daunting. The example provided involves recognizing the digit 'three' to demonstrate how neural networks mimic the brain's ability to interpret complex patterns through neural connections and synapses.

05:02

📘 Yandex Practicum Python Developer Course

The second paragraph shifts focus to promote a Python Developer course offered by Yandex Practicum, featuring a free introductory part. The course spans nine months, aiming to equip learners with skills necessary for a Python programmer today. It covers backend development, interaction with applications and users, writing first codes, database design with Django, and advanced programming algorithms, including work on real projects. Furthermore, Yandex Practicum offers career support by helping students build portfolios, prepare for interviews, and write resumes, presenting this course as a valuable opportunity for aspiring programmers.

10:03

🔬 Understanding Neural Networks and Bias Neurons

This part delves into the practical aspects of neural networks, beginning with the significance of bias neurons that enable shifts in the activation function's graph, enhancing the network's flexibility in pattern recognition. It also introduces the EMNIST dataset, comprising 60,000 images of handwritten digits used for training. The narrative then guides through preparing this dataset for neural network training, emphasizing color inversion and reshaping to fit the input layer of the neural network, laying the groundwork for implementing the network in Python.

15:04

👨‍🏫 Training the Neural Network

This section focuses on the neural network training process, detailing steps such as initializing weights, forward propagation for processing input data through layers, applying activation functions, and calculating losses using mean squared error. It stresses the importance of adjusting the weights during training to improve accuracy, a process encapsulated within the backpropagation algorithm. This intricate process is simplified to six lines of code, demonstrating the network's learning capability by achieving 93% accuracy in recognizing handwritten digits, underscoring the practical application and effectiveness of the training method outlined.

💾 Saving and Testing the Neural Network

The final paragraph discusses saving the trained model for future use and extending it into a custom neural network library, suggesting potential for broader application and ease of use. It concludes with testing the model on a newly created digit image outside the training set, validating the network's ability to generalize and recognize digits not previously encountered during training. This test serves as proof of the neural network's learning efficiency and adaptability, marking a successful end to the tutorial.

Mindmap

Highlights

Introduction to building a neural network from scratch in Python.

Explanation of basic neural network principles.

Introduction to neuron bias and different activation functions.

Demonstration on how to save a trained neural network model.

Guidance on performing inference with a trained model.

Instructions for packaging a neural network into a library.

Utilizing the MNIST dataset to teach neural network handwritten digit recognition.

Explanation of neural network layers and neuron connections.

Insights into the importance of random initialization for weights.

Tutorial on loading and processing the MNIST dataset for neural network training.

Details on the significance of the neuron bias in neural networks.

Comparison of various activation functions.

Step-by-step guide to neural network training process.

Introduction to backpropagation and weight adjustment.

Results showcasing the neural network's ability to recognize handwritten digits with high accuracy.

Practical demonstration of neural network inference with custom images.