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Total Size:
111.5 MB
Info Hash:
7B7A63C17CED2F66E7AFD0841F669F7521673482
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Added:
Nov. 2, 2025, 9:30 a.m.
Stats:
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(Last updated: Nov. 2, 2025, 9:33 a.m.)
| File | Size |
|---|---|
| Nauman M. Keras 3. Hands-On Deep Learning with Python, Neural Networks,...2025.pdf | 14.6 MB |
| Code.zip | 97.0 MB |
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111.5 MB
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2025-11-02
| Uploaded by andryold1 | Size 111.5 MB | Health [ 41 /29 ] | Added 2025-11-02 |
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SOURCE: Nauman M. Keras 3. Hands-On Deep Learning with Python, Neural Networks,...2025
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COVER
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MEDIAINFO
Textbook in PDF format Harness the power of AI with this guide to using Keras! Start by reviewing the fundamentals of deep learning and installing the Keras API. Next, follow Python code examples to build your own models, and then train them using classification, gradient descent, and regularization. Design large-scale, multilayer models and improve their decision making with reinforcement learning. With tips for creating generative AI models, this is your cutting-edge resource for working with deep learning! Learn to use Keras for deep learning Work with techniques such as gradient descent, classification, regularization, and more Build and train convolutional neural networks, transformers, and autoencoders Deep Learning Basics Understand the foundations of deep learning, machine learning, and neural networks. Learn core concepts like gradient descent, classification, and regularization to fine-tune your models and minimize loss function. Model Development and Training Follow step-by-step instructions to build models in Keras: develop a convolutional neural network, apply the functional API for complex models, and implement transformer architecture. Use reinforcement learning to improve your models’ decision-making. Generative AI Models Build and train your own generative AI models! Get hands-on with text to image techniques and work with variational autoencoders and generative adversarial networks. Neural networks Gradient descent Classification Regularization Convolutional neural networks (CNNs) Functional API Transformer architecture Reinforcement learning Autoencoders Stable Diffusion
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