Deep Learning for Beginners: A beginner’s guide to getting up and running with deep learning from scratch using Python

Price: $59.84
(as of Dec 17,2024 15:38:59 UTC – Details)

ASIN ‏ : ‎ B086D29S1K
Publisher ‏ : ‎ Packt Publishing; 1st edition (September 18, 2020)
Publication date ‏ : ‎ September 18, 2020
Language ‏ : ‎ English
File size ‏ : ‎ 67389 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 434 pages
Page numbers source ISBN ‏ : ‎ 1838640851

Deep Learning for Beginners: A beginner’s guide to getting up and running with deep learning from scratch using Python

Are you interested in diving into the world of deep learning but not sure where to start? Look no further! In this beginner’s guide, we will walk you through the basics of deep learning and how to get started with Python.

What is deep learning?

Deep learning is a subset of machine learning that involves training artificial neural networks to perform tasks such as image recognition, speech recognition, and natural language processing. It is inspired by the structure and function of the human brain, with networks of interconnected nodes that can learn from and make predictions on data.

Getting started with Python

Python is a popular programming language for deep learning due to its simplicity and versatility. To get started, you will need to install Python and a few key libraries, such as NumPy, pandas, and TensorFlow. These libraries will allow you to manipulate and analyze data, as well as build and train neural networks.

Building your first neural network

Once you have Python set up, you can start building your first neural network. This can be as simple as a basic feedforward neural network with a few layers, or as complex as a convolutional neural network for image recognition. There are many tutorials and resources available online to help you get started, so don’t be afraid to experiment and learn as you go.

Training your neural network

Training a neural network involves feeding it labeled data and adjusting the network’s weights and biases to minimize the error between the predicted and actual outputs. This process can be time-consuming and resource-intensive, so it is important to have a good understanding of your data and network architecture before starting training.

Testing and evaluating your model

Once your neural network is trained, you can test it on new data to see how well it performs. You can use metrics such as accuracy, precision, and recall to evaluate the model’s performance and make improvements as needed. Remember that deep learning is an iterative process, so don’t be discouraged if your first model doesn’t perform as well as you’d like.

In conclusion, deep learning is a powerful tool for solving complex problems in a variety of domains. By following this beginner’s guide and experimenting with Python and neural networks, you can start your journey into the exciting world of deep learning. Happy coding!
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