Neural Network Development: Free Educational Materials and Open-Source Projects

Collected everything in one place! A selection of educational materials on neural networks, as well as various open source projects with neural networks that can be useful for developing services!

Educational materials

Neural Networks Basics

• Introduction game to Neural Networks
• Introduction to Neural Networks
• A Gentle Introduction to Deep Learning
• Neural Networks Explained
• Deep Learning Fundamentals

Machine Learning Libraries

• TensorFlow Beginner's Guide
• PyTorch Tutorials for Beginners
• Keras Official Documentation
• scikit-learn: Machine Learning in Python
• Fastai: Practical Deep Learning for Coders

Neural Network Architectures

• Understanding Feedforward Neural Networks
• Introduction to Convolutional Neural Networks (CNNs)
• Exploring Recurrent Neural Networks (RNNs)
• Dive into Long Short-Term Memory (LSTM) Networks
• Introduction to Gated Recurrent Units (GRUs)

Deep Learning Frameworks

• Dive into TensorFlow 2.0
• Hands-on with PyTorch for Deep Learning
• Building Neural Networks with Keras
• MXNet Deep Learning Cookbook

Activation Functions and Optimization

• Activation Functions in Neural Networks
• Optimizing Neural Networks: An Overview
• Gradient Descent and Backpropagation
• Exploring Adam Optimizer
• Regularization Techniques for Neural Networks

Loss Functions and Evaluation

• Common Loss Functions in Neural Networks
• Evaluating Model Performance: Accuracy, Precision, Recall
• ROC Curves and AUC Score Explained
• Cross-Validation for Model Assessment

Data Preprocessing for Neural Networks

• Data Normalization and Standardization
• Handling Categorical Data in Neural Networks
• Data Augmentation Techniques
• Dealing with Imbalanced Datasets

Neural Network Training Tips

• Strategies for Training Deep Learning Models
• Learning Rate Schedules and Adaptive Learning Rates
• Early Stopping and Model Checkpointing
• Exploding and Vanishing Gradients: Solutions

Advanced Topics in Neural Networks

• Generative Adversarial Networks (GANs) Introduction
• Transfer Learning and Fine-Tuning
• Exploring Neural Style Transfer
• Introduction to Autoencoders

Resources for Interview Preparation

• Common Neural Network Interview Questions
• Deep Learning Interview Preparation Guide

Online Courses and Tutorials

• Stanford University's CS231n: Convolutional Neural Networks
• Deep Learning Specialization on Coursera
• Fast.ai Practical Deep Learning for Coders Course
• Introduction to Deep Learning with PyTorch

YouTube Channels for Neural Networks

• MLU-Explain
• 3Blue1Brown: Neural Network Visualizations
• Sentdex: Practical Neural Network Tutorials
• deeplizard: Deep Learning Simplified
• Tech with Tim: Neural Network Coding Demos

Practice and Challenges

• Kaggle: Machine Learning Competitions
• LeetCode for Machine Learning
• Codeforces Machine Learning Challenges

Remember, learning neural networks takes time and practice. Stay curious and keep exploring the exciting world of deep learning! 🚀🤖

Open-source projects

Open-source projects on GitHub that cover a variety of neural network applications, including GANs for music, image, deepfakes, recommendation systems, language models, and more:

Text and Language Models

• Llama2 by Meta: The third iteration of the Generative Pre-trained Transformer, capable of performing a variety of language tasks.
• GPT-3 by OpenAI: The third iteration of the Generative Pre-trained Transformer, capable of performing a variety of language tasks.
• transformers by Hugging Face: A library for state-of-the-art natural language processing tasks, including pretrained models like BERT, GPT-2, and more.

Image Generation and Manipulation:

• Stable Diffusion: A latent text-to-image diffusion model.
• CycleGAN by Jun-Yan Zhu: Unpaired image-to-image translation using Cycle-Consistent Adversarial Networks.
• BigGAN by Google Research: Large-scale GAN for high-fidelity image synthesis.
• StyleGAN2 by NVlabs: Improved version of StyleGAN for high-quality image synthesis.

Music Generation and Audio Processing:

• Audiocraft by Google Research: Audiocraft is a library for audio processing and generation with deep learning.
• Magenta by Google Research: Research project exploring music and art generation with machine learning.
• DDSP by Google Magenta: Differentiable Digital Signal Processing library for audio synthesis and processing.

Recommendation Systems:

• LightFM: Hybrid recommendation algorithms in Python.
• Surprise: A Python scikit for building and analyzing recommender systems.

GANs for Deepfakes:

• Wunjo AI: Open-source project to create deepfake animation without unlimited.
• DeepFaceLab: Deepfake creation and face swapping toolset utilizing GANs.
• FSGAN by NVIDIA: Few-shot Unsupervised Image-to-Image Translation for deepfake generation.

Medical Imaging:

• fastai Medical Imaging: fastai-based medical image analysis toolkit.
• TensorFlow Medical Imaging: TensorFlow-based models for medical imaging tasks.

Robotics and Reinforcement Learning:

• Robosuite: A simulator for robot learning and control.
• OpenAI Gym: Toolkit for developing and comparing reinforcement learning algorithms.

Video Analysis and Processing:

• DeOldify: A Deep Learning based project for colorizing and restoring old images.
• Image Inpainting: Deep learning-based image inpainting for video editing and restoration.

Style Transfer and Art Generation:

• Fast Style Transfer: TensorFlow-based implementation of fast style transfer techniques.
• Neural Style: Torch-based neural style transfer implementation.

These projects cover a wide range of neural network applications and can serve as valuable resources for learning and experimentation. Remember to review the projects' documentation and licenses before usage.

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Owner: Wladislav Radchenko

Email: i@wladradchenko.ru

                             

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