Tag: Keras

January 3, 2025

Neural Networks with TensorFlow and Keras

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Free Download Neural Networks with TensorFlow and Keras: Training, Generative Models, and Reinforcement Learning
English | 2024 | ASIN : B0DG5DBSK3 | 173 Pages | PDF EPUB (True) | 17.4 MB
The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience.

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January 1, 2025

Neural Networks with TensorFlow and Keras

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Free Download Neural Networks with TensorFlow and Keras: Training, Generative Models, and Reinforcement Learning
English | 2024 | ASIN : B0DG5DBSK3 | 173 Pages | PDF | 6 MB
The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience.

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September 10, 2024

Applied Deep Learning with Keras

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Free Download Applied Deep Learning with Keras: Solve complex real-life problems with the simplicity of Keras
English | 2019 | ISBN: 9781838555078 | 412 Pages | PDF EPUB (True) | 25 MB
Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.

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September 10, 2024

Applied Deep Learning with Keras

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Free Download Applied Deep Learning with Keras: Solve complex real-life problems with the simplicity of Keras
English | 2019 | ISBN: 9781838555078 | 412 Pages | PDF EPUB (True) | 25 MB
Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.

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March 6, 2024

Deep Learning with Python and Keras Build a Model For Sentiment Analysis

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Free Download Deep Learning with Python and Keras Build a Model For Sentiment Analysis
Released 2/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Advanced | Genre: eLearning | Language: English + srt | Duration: 1h 55m | Size: 207 MB
Learn to apply sentiment analysis to your problems through a practical, real world use case. In this course, certified Google cloud architect and data engineer Janani Ravi guides you through the process of building and training a RNN to do sentiment analysis, including validating your results. Go over how to preprocess text for sentiment analysis, as well as approaches you can use and challenges you may encounter. Get set up with Google Colab and import Python modules and loading data, then learn how to analyze word lengths, clean and preprocess text, and visualize text with word clouds. Explore feed-forward neural networks, then dive into configuring, training, and evaluating your dense neural network (DNN). Plus, learn how to train RNNs and LSTNs.

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