Deep learning and machine learning are areas of technology that have been in the spotlight in recent years, and they have made breakthroughs in various industries. In order to gain a deeper understanding of the relevant comments and opinions, and to succeed in this field, you need to know the following key information:
1.Understand the basic concepts:
Machine Learning:
Machine learning is a branch of artificial intelligence that enables computers to learn from data and improve automatically.
Machine learning algorithms can be used for a variety of tasks, including classification, regression, clustering,**, and anomaly detection.
Machine learning is the technology that trains computers to learn from data and make decisions or decisions.
Common machine learning algorithms include linear regression, decision trees, support vector machines, naïve Bayes, and random forests.
Deep Learning:
Deep learning is a subset of machine learning that uses artificial neural networks (ANNs) to learn complex patterns in data.
An artificial neural network consists of multiple layers, each containing multiple interconnected nodes.
Deep learning models can learn from large amounts of data and have made breakthroughs in image recognition, natural language processing, speech recognition, and other fields.
Artificial Neural Networks:A computational model inspired by the structure of the human brain that consists of multiple layers, each containing many interconnected nodes (called neurons).
2.Learn how they differ:
Machine learning algorithms often require artificial feature engineering to extract important features from the data, while deep learning algorithms can automatically learn features through neural networks.
Deep learning models typically require large amounts of data to train, while machine learning models can use less data.
Deep learning models can solve more complex tasks, such as image recognition, natural language processing, etc., while machine learning models are often used for simpler tasks such as classification, regression, etc.
3.Master the relevant technology:
Programming languages: Python, J**A, C++, etc.
Machine learning frameworks: TensorFlow, PyTorch, scikit-learn, etc.
Deep learning models: CNN, RNN, GAN, etc.
Mathematics: Linear algebra, probability theory, statistics, etc.
4.Stay up to date with the latest developments:
Read related books, **, and blog posts.
Participate in academic conferences and training courses.
Follow industry news and trends.
5.Practical Application:
Try building your own deep learning model.
Participate in open source projects.
Apply deep learning techniques to real-world problems.
References:
Books:
Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville
Machine Learning by Zhihua Zhou.
Data analytics is the foundation of machine learningMastering data analysis skills can help you better understand and apply machine learning algorithms.
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Reason for recommendation:
Suitable for beginners, the content is step-by-step and easy to understand;
Combined with a large number of real cases, it helps readers apply theoretical knowledge to practice;
Teaching** and ** resources are provided to facilitate readers to learn and practice.
If you want to learn data analytics, this book is an excellent choice.
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Lessons:
Professor Andrew Ng's free online course: Machine Learning:
fast.AI's Deep Learning Course: Practical Deep Learning for Coders:
Google AI:
Microsoft Azure Machine Learning:
TensorFlow Tutorial:
Summary:
Deep learning and machine learning are dynamic technology areas with huge potential. By learning, mastering the technology, and practicing it, you can succeed in this field.
Finally, a few extra suggestions:
Don't be afraid to try new things and learn from your mistakes.
Actively participate in the community and learn from other developers.
Maintain a passion and curiosity for technology.