Your cart is currently empty!
Tag: Regression
STAT2: Modeling with Regression and – Hardcover, by Cannon Ann; Cobb – Very Good
STAT2: Modeling with Regression and – Hardcover, by Cannon Ann; Cobb – Very Good
Price : 146.50
Ends on : N/A
View on eBay
If you’re looking for a comprehensive guide to statistical modeling with regression, look no further than “STAT2: Modeling with Regression and – Hardcover” by Cannon Ann and Cobb. This book is in very good condition and is perfect for anyone looking to dive deeper into the world of regression analysis. With clear explanations and examples, this book will help you master the art of modeling with regression. Don’t miss out on this valuable resource for your statistical toolkit!
#STAT2 #Modeling #Regression #Hardcover #Cannon #Ann #Cobb #Good,ann
Hands-On Machine Learning from Scratch: Develop a Deeper Understanding of Machine Learning Models by Implementing Them from Scratch in Python (Linear Regression, … Random Forest, Neural Networks & more)
Price: $31.81
(as of Jan 02,2025 03:19:02 UTC – Details)
ASIN : B07TKYBGJD
Publication date : June 27, 2019
Language : English
File size : 7135 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 : 146 pages
Hands-On Machine Learning from Scratch: Develop a Deeper Understanding of Machine Learning Models by Implementing Them from Scratch in Python (Linear Regression, Random Forest, Neural Networks & more)Are you looking to take your machine learning skills to the next level? Do you want to truly understand how popular machine learning models work under the hood? If so, then this hands-on course is for you!
In this course, you will learn how to implement popular machine learning models from scratch using Python. You will start with simple models like linear regression and gradually move on to more complex models like random forests and neural networks.
By implementing these models from scratch, you will develop a deeper understanding of how they work and how to fine-tune them for better performance. You will also gain valuable insights into the inner workings of machine learning algorithms, which will help you in your future projects.
Whether you are a beginner looking to get started with machine learning or an experienced data scientist looking to deepen your understanding of machine learning models, this course is for you. So don’t wait, enroll now and take your machine learning skills to the next level!
#HandsOn #Machine #Learning #Scratch #Develop #Deeper #Understanding #Machine #Learning #Models #Implementing #Scratch #Python #Linear #Regression #Random #Forest #Neural #Networks,deep learning for nlp and speech recognition
Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP
Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP
Price : 42.64
Ends on : N/A
View on eBay
Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLPIn this post, we will explore the exciting world of deep learning with TensorFlow 2 and Keras, two powerful libraries that have revolutionized the field of artificial intelligence. We will delve into various advanced topics such as regression, Convolutional Neural Networks (ConvNets), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Natural Language Processing (NLP).
Regression is a common task in machine learning where the goal is to predict a continuous value based on input features. We will learn how to implement regression models using TensorFlow 2 and Keras, and explore techniques such as feature scaling, regularization, and hyperparameter tuning.
ConvNets are a type of neural network that is commonly used for image recognition tasks. We will build ConvNets using TensorFlow 2 and Keras, and train them on popular datasets such as CIFAR-10 and ImageNet. We will also explore techniques for visualizing and interpreting ConvNet models.
GANs are a fascinating type of neural network that can generate realistic-looking images, music, and text. We will implement GANs using TensorFlow 2 and Keras, and train them on datasets such as MNIST and CelebA. We will also discuss techniques for training stable and high-quality GANs.
RNNs are a type of neural network that is well-suited for sequential data such as time series and natural language. We will build RNNs using TensorFlow 2 and Keras, and train them on datasets such as IMDb reviews and Twitter tweets. We will also explore advanced RNN architectures such as LSTMs and GRUs.
NLP is a rapidly growing field that focuses on understanding and generating human language. We will explore NLP tasks such as sentiment analysis, named entity recognition, and text generation using TensorFlow 2 and Keras. We will also discuss techniques for preprocessing text data, building word embeddings, and fine-tuning pre-trained language models.
By the end of this post, you will have a solid understanding of deep learning with TensorFlow 2 and Keras, and be able to build and train advanced neural network models for a wide range of tasks. So let’s dive in and start exploring the exciting world of deep learning!
#Deep #Learning #TensorFlow #Keras #Regression #ConvNets #GANs #RNNs #NLP
Deep Learning with TensorFlow 2 and Keras – Second Edition: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API
Price:$43.99– $41.78
(as of Dec 29,2024 00:05:03 UTC – Details)
From the brand
Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.
Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.
We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.
New Releases
LLMs and Generative AI
Machine Learning
See Our Full Range
Publisher : Packt Publishing; 2nd ed. edition (December 20, 2019)
Language : English
Paperback : 646 pages
ISBN-10 : 1838823417
ISBN-13 : 978-1838823412
Item Weight : 2.44 pounds
Dimensions : 9.25 x 7.52 x 1.33 inches
Are you ready to take your deep learning skills to the next level? Look no further than the newly released Second Edition of “Deep Learning with TensorFlow 2 and Keras”! This comprehensive guide covers everything from regression to ConvNets, GANs, RNNs, NLP, and more using TensorFlow 2 and the Keras API.Whether you’re a beginner looking to dive into the world of deep learning or an experienced practitioner seeking to enhance your skills, this book has something for everyone. With hands-on examples, practical exercises, and real-world applications, you’ll learn how to build and train deep learning models for a wide range of tasks.
Don’t miss out on the opportunity to master the latest techniques in deep learning with TensorFlow 2 and Keras. Get your copy of “Deep Learning with TensorFlow 2 and Keras – Second Edition” today and start building cutting-edge AI applications!
#Deep #Learning #TensorFlow #Keras #Edition #Regression #ConvNets #GANs #RNNs #NLP #TensorFlow #Keras #API,rnn
The Regression Model of Machine Translation: Learning, Instance Selection, Decoding, and Evaluation
Price: $94.00
(as of Dec 28,2024 16:03:38 UTC – Details)
Publisher : LAP LAMBERT Academic Publishing (November 4, 2011)
Language : English
Paperback : 172 pages
ISBN-10 : 3846507490
ISBN-13 : 978-3846507490
Item Weight : 9.7 ounces
Dimensions : 5.91 x 0.39 x 8.66 inches
Machine translation has come a long way in recent years, thanks to the development of advanced models like the regression model. This model, which is based on statistical learning techniques, has revolutionized the way we approach translation tasks.The regression model of machine translation involves four key components: learning, instance selection, decoding, and evaluation. Let’s take a closer look at each of these components:
1. Learning: In the learning phase, the regression model is trained on a large dataset of parallel texts in different languages. This training data helps the model learn the relationships between words and phrases in the source and target languages, allowing it to make accurate translations.
2. Instance selection: Once the model has been trained, it must be able to select the most relevant instances from the training data to use during translation. This process involves identifying key features of the input text and selecting instances that are most likely to result in accurate translations.
3. Decoding: During the decoding phase, the regression model uses the selected instances to generate translations of the input text. This process involves selecting the most likely translation based on the learned relationships between words and phrases in the source and target languages.
4. Evaluation: Finally, the output of the regression model is evaluated to determine its accuracy and performance. This evaluation process involves comparing the model’s translations to human-generated translations and measuring factors like fluency, accuracy, and adequacy.
Overall, the regression model of machine translation offers a powerful and effective approach to translating text between languages. By combining statistical learning techniques with advanced algorithms, this model is able to produce high-quality translations that rival those generated by human translators. As machine translation continues to evolve, the regression model will undoubtedly play a key role in shaping the future of cross-lingual communication.
#Regression #Model #Machine #Translation #Learning #Instance #Selection #Decoding #Evaluation
Tensorflow for Deep Learning: From Linear Regression to Reinforcement Learning
Tensorflow for Deep Learning: From Linear Regression to Reinforcement Learning
Price : 10.24
Ends on : N/A
View on eBay
Tensorflow is one of the most popular frameworks for deep learning, offering a wide range of tools and functionalities for building and training neural networks. In this post, we will explore how Tensorflow can be used for various deep learning tasks, starting from simple linear regression all the way to more complex reinforcement learning algorithms.Linear regression is often considered the starting point for understanding machine learning and deep learning concepts. With Tensorflow, implementing a linear regression model is straightforward and can serve as a great introduction to the framework. By defining the model architecture, loss function, and optimization algorithm, one can easily train a linear regression model using Tensorflow.
Moving on to more complex tasks, Tensorflow also provides tools for building and training convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for tasks such as image classification and natural language processing. These models can be easily implemented using Tensorflow’s high-level API, making it easier for beginners to get started with deep learning.
For those interested in reinforcement learning, Tensorflow offers libraries and tools for implementing algorithms such as deep Q-learning and policy gradients. Reinforcement learning involves training agents to interact with an environment and learn optimal strategies through trial and error. With Tensorflow, one can easily build and train reinforcement learning models for various tasks, such as playing video games or controlling robotic systems.
In conclusion, Tensorflow is a versatile framework that can be used for a wide range of deep learning tasks, from simple linear regression to complex reinforcement learning algorithms. Whether you are a beginner looking to learn the basics of deep learning or an experienced researcher exploring advanced techniques, Tensorflow provides the tools and resources needed to build and train powerful neural networks.
#Tensorflow #Deep #Learning #Linear #Regression #Reinforcement #Learning
Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP
Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP
Price : 47.94
Ends on : N/A
View on eBay
Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLPIn this post, we will dive into the world of deep learning using two powerful libraries: TensorFlow 2 and Keras. We will explore various advanced topics including regression, Convolutional Neural Networks (ConvNets), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Natural Language Processing (NLP).
TensorFlow 2 is the latest version of Google’s popular open-source deep learning library, which offers a high-level API for building and training deep learning models. Keras, on the other hand, is a high-level neural networks API that can run on top of TensorFlow, making it easier to build and experiment with deep learning models.
Regression is a common task in machine learning where the goal is to predict a continuous value based on input features. We will demonstrate how to build a regression model using TensorFlow 2 and Keras, and train it on a dataset to make predictions.
ConvNets are a type of deep neural network that are particularly well-suited for image recognition tasks. We will show how to create a ConvNet model using TensorFlow 2 and Keras, and train it on a dataset to classify images.
GANs are a type of deep learning model that can generate new data samples, such as images, that are similar to a given dataset. We will explore how to build a GAN using TensorFlow 2 and Keras, and train it to generate realistic images.
RNNs are a type of neural network that are designed to handle sequential data, such as time series or text. We will demonstrate how to build an RNN model using TensorFlow 2 and Keras, and train it on a dataset to perform sequence prediction.
NLP is a branch of artificial intelligence that focuses on the interaction between computers and human language. We will show how to build an NLP model using TensorFlow 2 and Keras, and train it on a dataset to perform tasks like sentiment analysis or text generation.
Overall, this post will provide a comprehensive overview of deep learning techniques using TensorFlow 2 and Keras, covering a wide range of applications from regression to NLP. Stay tuned for more in-depth tutorials and examples on each of these topics!
#Deep #Learning #TensorFlow #Keras #Regression #ConvNets #GANs #RNNs #NLP
Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for Beginners
Price: $13.59
(as of Dec 27,2024 09:18:42 UTC – Details)
Publisher : CreateSpace Independent Publishing Platform (September 26, 2018)
Language : English
Paperback : 100 pages
ISBN-10 : 1727618572
ISBN-13 : 978-1727618570
Item Weight : 5.1 ounces
Dimensions : 6 x 0.23 x 9 inches
Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database Querying, and Machine Learning for BeginnersData science has become a crucial aspect of business operations in today’s data-driven world. From predicting customer behavior to optimizing supply chain management, businesses are increasingly relying on data science techniques to gain valuable insights and make informed decisions.
In this post, we will delve into some key concepts of data science that are essential for beginners looking to harness the power of data for their business. These include predictive modeling, data mining, data analytics, data warehousing, data visualization, regression analysis, database querying, and machine learning.
Predictive modeling involves using historical data to make predictions about future events. By analyzing patterns and relationships in the data, businesses can forecast customer behavior, sales trends, and market demand, among other things.
Data mining is the process of extracting valuable insights from large datasets. By using techniques such as clustering, classification, and association analysis, businesses can uncover hidden patterns and trends that can inform strategic decision-making.
Data analytics involves the use of statistical techniques to analyze and interpret data. By measuring and evaluating key performance indicators, businesses can identify areas of improvement and optimize their operations.
Data warehousing is the process of storing and managing large volumes of data in a centralized repository. By organizing data in a structured manner, businesses can easily access and analyze information for reporting and decision-making purposes.
Data visualization is the presentation of data in a visual format, such as charts, graphs, and dashboards. By visually representing data, businesses can quickly identify trends, patterns, and outliers that may not be apparent in raw data.
Regression analysis is a statistical technique used to model the relationship between variables. By analyzing the impact of one or more independent variables on a dependent variable, businesses can make predictions and optimize their processes.
Database querying involves retrieving and manipulating data from databases using SQL (Structured Query Language). By writing queries to extract specific information, businesses can access and analyze data to support decision-making.
Machine learning is a branch of artificial intelligence that involves building algorithms that can learn from and make predictions based on data. By training machine learning models on historical data, businesses can automate processes, make predictions, and optimize their operations.
In conclusion, data science is a powerful tool for businesses looking to gain a competitive edge in today’s data-driven world. By leveraging techniques such as predictive modeling, data mining, data analytics, data warehousing, data visualization, regression analysis, database querying, and machine learning, beginners can harness the power of data to make informed decisions and drive business growth.
#Data #Science #Business #Predictive #Modeling #Data #Mining #Data #Analytics #Data #Warehousing #Data #Visualization #Regression #Analysis #Database #Querying #Machine #Learning #Beginners
Mike’s Peanuts: Machine Learning For Kids: Linear Regression (Machine Learning Series: Mike’s Peanuts)
Price: $14.00
(as of Dec 27,2024 04:57:42 UTC – Details)
Publisher : Rocket Baby Club (July 26, 2018)
Language : English
Hardcover : 38 pages
ISBN-10 : 1643708740
ISBN-13 : 978-1643708744
Reading age : Baby – 12 years
Grade level : Kindergarten and up
Item Weight : 11.4 ounces
Dimensions : 8.5 x 0.25 x 8.5 inches
Mike’s Peanuts: Machine Learning For Kids: Linear Regression (Machine Learning Series: Mike’s Peanuts)In our latest installment of Mike’s Peanuts Machine Learning Series, we will be exploring the concept of linear regression. Linear regression is a fundamental concept in machine learning that is used to predict the relationship between two variables.
In this episode, we will use the example of Mike’s Peanuts sales data to demonstrate how linear regression can be used to predict future sales based on past data. We will walk through the steps of collecting and analyzing the data, building a linear regression model, and making predictions.
Linear regression is a powerful tool that can be used to make informed decisions and improve business outcomes. By understanding the basics of linear regression, kids can develop valuable skills in data analysis and prediction.
Stay tuned for more episodes in the Mike’s Peanuts Machine Learning Series, where we will continue to explore different machine learning concepts in a fun and engaging way. Let’s start learning and predicting with Mike’s Peanuts!
#Mikes #Peanuts #Machine #Learning #Kids #Linear #Regression #Machine #Learning #Series #Mikes #Peanuts,machine learning
Deep Learning with TensorFlow 2 and Keras – Second Edition: Regression, ConvN…
Deep Learning with TensorFlow 2 and Keras – Second Edition: Regression, ConvN…
Price : 48.31
Ends on : N/A
View on eBay
Deep Learning with TensorFlow 2 and Keras – Second Edition: Regression, ConvNets, and GANsIn the world of artificial intelligence and machine learning, deep learning has emerged as a powerful tool for solving complex problems. TensorFlow, an open-source machine learning framework developed by Google, has become one of the most popular libraries for building deep learning models. Keras, a high-level neural networks API written in Python, has also gained widespread adoption for its simplicity and ease of use.
The second edition of the book “Deep Learning with TensorFlow 2 and Keras” explores the latest advancements in deep learning techniques using TensorFlow 2 and Keras. The book covers a wide range of topics, including regression, convolutional neural networks (ConvNets), and generative adversarial networks (GANs).
Readers will learn how to build and train regression models for predicting continuous values, such as house prices or stock prices. They will also delve into the world of ConvNets, a type of deep neural network that is particularly well-suited for image recognition tasks. The book will cover how to implement ConvNets using TensorFlow 2 and Keras, and how to fine-tune them for optimal performance.
Finally, readers will explore the exciting world of GANs, a type of neural network architecture that can generate realistic images and videos. The book will cover how to build and train GANs using TensorFlow 2 and Keras, and how to use them for tasks such as image generation and style transfer.
Whether you are a beginner looking to get started with deep learning or an experienced practitioner looking to expand your skills, “Deep Learning with TensorFlow 2 and Keras – Second Edition” is a comprehensive guide that will help you master the latest techniques in deep learning.
#Deep #Learning #TensorFlow #Keras #Edition #Regression #ConvN.., deep learning
