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From the Publisher
Neural Networks with Python
Chapters You Must Read..
Python, TensorFlow, and your First Neural Network
Deep Dive into Feedforward Networks
Convolutional Networks for Visual Tasks
Recurrent Networks for Sequence Data
Data Generation with GANs
Transformers for Complex Tasks
Autoencoders for Data Compression and Generation
Capsule Networks
Power of Python, TensorFlow and Keras to build strong Deep Leaning Models
What’s unique about this book is that we’ll also focus on the problems you might face while building these networks. We’ll look at how to troubleshoot them and even how to fine-tune your models. By the end of it, you won’t just know how to build a neural network; you’ll know what to do when things don’t go as planned.
If you’re up for a rollercoaster ride through the incredible world of neural networks, hold tight. Grab a cup of coffee, open up your favorite code editor, and let’s get started.
Gain flexibility with diverse neural network architectures for various problems.
Hands-on experience in building, training, and fine-tuning neural networks.
Learn strategic approaches for troubleshooting and optimizing neural models.
Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms.
Acquire skills in crucial data preprocessing and augmentation techniques.
Understand and apply optimization techniques and hyperparameter tuning.
Implement an end-to-end machine learning project, from data to deployment.
Master Python for machine learning, from setup to complex models.
ASIN : B0CMNYBF2Q
Publisher : GitforGits; 1st edition (November 3, 2023)
Publication date : November 3, 2023
Language : English
File size : 955 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 152 pages
Page numbers source ISBN : 8119177487
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Neural Networks with Python: Design CNNs, Transformers, GANs and capsule networks using tensorflow and keras
In this post, we will delve into the world of Neural Networks and explore the various architectures that can be designed using Python, specifically with the help of tensorflow and keras libraries.
Convolutional Neural Networks (CNNs) are widely used in image recognition tasks, and we will cover how to design and train CNNs using tensorflow and keras. We will also discuss how to fine-tune pre-trained models for specific tasks.
Transformers have gained popularity in natural language processing tasks, and we will explore how to implement transformers for tasks such as text classification and language translation.
Generative Adversarial Networks (GANs) are used for generating new data samples, and we will cover how to design and train GANs using tensorflow and keras. We will also discuss how to evaluate the performance of GANs.
Capsule networks are a relatively new architecture that has shown promise in tasks such as image classification and object detection. We will explore how to design and train capsule networks using tensorflow and keras.
By the end of this post, you will have a comprehensive understanding of how to design and train various neural network architectures using tensorflow and keras in Python. Stay tuned for more in-depth tutorials and practical examples!
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