Packt's Book Sales

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Packt has recently published a video, Dynamic Neural Network Programming with PyTorch, demonstrates how you can train your networks faster with PyTorch.

Deep learning influences key aspects of core sectors such as IT, finance, science, and many more. Problems arise when it comes to getting computational resources for your network. You need to have a powerful GPU and plenty of time to train a network for solving a real-world task.

What you will learn?

• Get familiar with PyTorch fundamentals while learning to code a deep neural network in Python
  
• Create any task-oriented extension very quickly with the easy-to-use PyTorch interface
  
• Perform image captioning and grammar parsing using Natural Language Processing
  
• Use a computational graph and run it in parallel in the target GPU
  
• Create unique C++/CUDA extensions for PyTorch that work on CPU and GPU
  
• Use powerful toolkits from Python library while solving NLP or image recognition tasks
  

The Author

Anastasia Yanina is a Senior Data Scientist with around 5 years' experience. She is an expert in Deep Learning and Natural Language processing and constantly develops her skills as far as possible. She is passionate about human-to-machine interactions. She believes that bridging the gap may become possible with deep neural network architectures.

[Packt]

Packt has recently published a book Hands-On Unsupervised Learning with Python, which helps to discover the skill-sets required to implement various approaches to Machine Learning with Python.

You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.

What You Will Learn?

• Use cluster algorithms to identify and optimize natural groups of data
  
• Explore advanced non-linear and hierarchical clustering in action
  
• Soft label assignments for fuzzy c-means and Gaussian mixture models
  
• Detect anomalies through density estimation
  
• Perform principal component analysis using neural network models
  
• Create unsupervised models using GANs
  
  

Author

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.

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Packt has recently published a book Python Machine Learning By Example - Second Edition Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn.

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML.

What You Will Learn

• Understand the important concepts in machine learning and data science
  
• Use Python to explore the world of data mining and analytics
  
• Scale up model training using varied data complexities with Apache Spark
  
• Delve deep into text and NLP using Python libraries such NLTK and gensim
  
• Select and build an ML model and evaluate and optimize its performance
  
• Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn
  
  Author
  
  Yuxi (Hayden) Liu is an author of a series of machine learning books and an education enthusiast. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller in Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt.

[Packt]

Packt has recently published a book Data Wrangling with Python which will help you simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices.

What You Will Learn:

- Use and manipulate complex and simple data structures
- Harness the full potential of DataFrames and numpy.array at run time
- Perform web scraping with BeautifulSoup4 and html5lib
- Execute advanced string search and manipulation with RegEX
- Handle outliers and perform data imputation with Pandas
- Use descriptive statistics and plotting techniques
- Practice data wrangling and modeling using data generation techniques


Authors:
Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in artificial intelligence and machine learning from Stanford and MIT.

Shubhadeep Roychowdhury
Shubhadeep Roychowdhury works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics. He holds a master’s degree in computer science from West Bengal University Of Technology and certifications in machine learning from Stanford.

[Packt]

Packt has recently published a book Data Visualization with Python which will help you understand, explore, and effectively present data using the powerful data visualization techniques of Python programming.

What You Will Learn

• Understand and use various plot types with Python
  Explore and work with different plotting libraries
  Learn to create effective visualizations
  Improve your Python data wrangling skills
  Hone your skill set by using tools like Matplotlib, Seaborn, and Bokeh
  Reinforce your knowledge of various data formats and representations
  

Authors
Mario Döbler
Mario Döbler is a Ph.D. student with focus in deep learning at the University of Stuttgart. He previously interned at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of deep learning, using state-of-the-art algorithms to develop cutting-edge products. In his master thesis, he dedicated himself to apply deep learning to medical data to drive medical applications.

Tim Großmann
Tim Größmann is a CS student with an interest in diverse topics, ranging from AI to IoT. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley, in the field of big data engineering. He's highly involved in different open source projects and actively speaks at meetups and conferences about his projects and experiences.

[Packt]

Packt has recently published a video,Hands-on Python for Finance , which will practically guide to use data-driven algorithms in Finance.

With numerous practical examples through the course, you will develop a full-fledged framework for Monte Carlo, which is a class of computational algorithms and simulation-based derivatives and risk analytics.

What You Will Learn

- General programing skills in Python and working with common Python interfaces
- Using Numpy, Pandas and matplotlib to manipulate, analyze and visualize data
- Understand the Time value of money applications and project selection
- Getting and with working data, time series forecasting methods and linear models
- Understand Correlation and portfolio construction
- Be comfortable with Monte Carlo Simulation, Value at Risk and Options Valuation

Author

Matthew Macarty

Matthew Macarty has taught graduate and undergraduate business school students for over 15 years and currently teaches at Bentley University. He has taught courses in statistics, quantitative methods, information systems and database design.

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Packt has recently published a video,Machine Learning for Algorithmic Trading Bots with Python which will help Financial practitioners to study machine learning and algorithmic trading.

This course covers the advances in the techniques developed for algorithmic trading and financial analysis based on the recent breakthroughs in machine learning.

What You Will Learn
- You will learn about financial terminology and methodology and how to apply them
- Get hands-on financial data structures and financial machine learning
- Understand complex financial terminology and methodology in simple ways
- Ensemble models and cross-validation for financial applications
- Backtesting for models and strategies evaluation and validation
- Apply your skills to real world cryptocurrency trading such as BitCoin and Ethereum
- Putting machine learning into real world problems and derive solutions

Author
Mustafa Qamar-ud-Din

Mustafa Qamar-ud-Din is a machine learning engineer with over 10 years of experience in the software development industry. He is a specialist in image processing, machine learning and deep learning. He worked with many startups and understands the dynamics of agile methodologies and the challenges they face on a day to day basis. He is also quite aware of the professional skills which the recruiters are looking for when making hiring decisions.

[martyl]

Hey, I am looking for a book which teaches how to create interactive maps, especially time dynamic maps. These are maps which have a slider, as you push the slider, the maps get updated to reflect the time you selected.

Any recommendation?

[Packt]

Packt has recently published a video,High-Performance Computing with Python 3.x which demonstrates how to build high-performance, distributed, and concurrent applications in Python.

This hands-on course covers all the important aspects of high-performance computing using Python 3.x. Throughout the course, we'll go over the various techniques, modules, frameworks, and architectures needed for high-performance computing. This course is designed with minimal theory and maximal practical implementation followed by step-by-step instructions to get you up-and-running.

What You Will Learn

• Use lambda expressions, generators, and iterators to speed up your code.
  
• A solid understanding of multiprocessing and multithreading in Python.
  
• Optimize performance and efficiency by leveraging NumPy, SciPy, and Cython for numerical computations.
  
• Load large data using Dask in a distributed setting.
  
• Leverage the power of Numba to make your Python programs run faster.
  
• Build reactive applications using Python.
  

Authors

Mohammed Kashif

Mohammed Kashif works as a Data Scientist at Nineleaps, India, dealing mostly with graph data analysis. Prior to this, he was working as a Python developer at Qualcomm. He completed his Master's degree in computer science from IIIT Delhi, with specialization in data engineering. His areas of interest include recommender systems, NLP, and graph analytics. In his spare time, he likes to solve questions on StackOverflow and help debug other people out of their misery. He is also an experienced teaching assistant with a demonstrated history of working in the higher-education industry.

[Packt]

Hands-On Blockchain for Python Developers

Learn how to implement real-world decentralized applications using Python, Vyper, Populus, and Ethereum.

What You Will Learn

• Understand blockchain technology and what makes it an immutable database
  
• Use the features of web3.py API to interact with the smart contract
  
• Create your own cryptocurrency and token in Ethereum using Vyper
  
• Use IPFS features to store content on the decentralized storage platform
  
• Implement a Twitter-like decentralized application with a desktop frontend
  
• Build decentralized applications in the shape of console, web, and desktop applications
  

Author

Arjuna Sky Kok

Arjuna Sky Kok has experience more than 10 years in expressing himself as a software engineer. He has developed web applications using Symfony, Laravel, Ruby on Rails, and Django. He also has built mobile applications on top of Android and iOS platforms.

Currently, he is researching Ethereum technology. Other than that, he teaches Android and iOS programming to students.

He graduated from Bina Nusantara University with majors in Computer Science and Applied Mathematics. He always strives to become a holistic person by enjoying leisure activities, such as dancing Salsa, learning French, and playing StarCraft 2. He lives quietly in the bustling city of Jakarta.

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Neural Network Projects with Python

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python.

What You Will Learn

• Learn various neural network architectures and its advancements in AI
  
• Master deep learning in Python by building and training neural network
  
• Master neural networks for regression and classification
  
• Discover convolutional neural networks for image recognition
  
• Learn sentiment analysis on textual data using Long Short-Term Memory
  
• Build and train a highly accurate facial recognition security system
  

Author

James Loy has more than five years, expert experience in data science in the finance and healthcare industries. He has worked with the largest bank in Singapore to drive innovation and improve customer loyalty through predictive analytics. He has also experience in the healthcare sector, where he applied data analytics to improve decision-making in hospitals. He has a master's degree in computer science from Georgia Tech, with a specialization in machine learning.

His research interest includes deep learning and applied machine learning, as well as developing computer-vision-based AI agents for automation in industry.

[Packt]

Hands-On Python for Finance

Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras

Key Takeaways

• Clean financial data with data preprocessing
  
• Visualize financial data using histograms, color plots, and graphs
  
• Perform time series analysis with pandas for forecasting
  
• Estimate covariance and the correlation between securities and stocks
  
• Optimize your portfolio to understand risks when there is a possibility of higher returns
  
• Calculate expected returns of a stock to measure the performance of a portfolio manager
  
• Create a prediction model using recurrent neural networks (RNN) with Keras and TensorFlow
  

About the Author

Krish Naik works as a lead data scientist, pioneering in machine learning, deep learning, and computer vision, and is an artificial intelligence practitioner, an educator, and a mentor, with over 7 years' experience in the industry. He also runs a YouTube channel where he explains various topics on machine learning, deep learning, and AI with many real-world problem scenarios. He has implemented various complex projects involving complex financial data with predictive modeling, machine learning, text mining, and sentiment analysis in the healthcare, retail, and e-commerce domains. He has delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums.

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Mastering OpenCV 4 with Python

Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.

Key Takeaways

• Handle files and images, and explore various image processing techniques
  
• Explore image transformations, including translation, resizing, and cropping
  
• Gain insights into building histograms
  
• Brush up on contour detection, filtering, and drawing
  
• Work with Augmented Reality to build marker-based and markerless applications
  
• Work with the main machine learning algorithms in OpenCV
  
• Explore the deep learning Python libraries and OpenCV deep learning capabilities
  
• Create computer vision and deep learning web applications
  

About the Author

Alberto Fernandez Villan is a software engineer with more than 12 years of experience in developing innovative solutions. In the last couple of years, he has been working in various project related to monitoring systems for industrial plants applying both IoT and Big Data technologies. He has a PhD in Computer Vision (2017), a Deep Learning Certification (2018) and has several publications in connection with computer vision and machine learning in journals such as Machine Vision and Applications, IEEE Transactions on Industrial Informatics, Sensors, IEEE Transactions on Industry Applications, IEEE Latin America Transactions, and more. Since 2013 he is a registered user ("albertofernandez") on the Q&A OpenCV forum with a dynamic activity.

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Building Serverless Microservices in Python

A practical guide for developing end-to-end serverless microservices in Python for developers, DevOps, and architects.

Key Takeaways

• Discover what microservices offer above and beyond other architectures
  
• Create a serverless application with AWS
  
• Gain secure access to data and resources
  
• Run tests on your configuration and code
  
• Create a highly available serverless microservice data API
  
• Build, deploy, and run your serverless configuration and code
  

About the Author

Richard Takashi Freeman has an M.Eng. in computer system engineering and a PhD in machine learning and natural language processing from the University of Manchester, United Kingdom. His current role is as a lead data engineer and architect, but he is also a data scientist and solutions architect. He has been delivering cloud-based, big data, machine learning, and data pipeline serverless and scalable solutions for over 14 years, and has spoken at numerous leading academic and industrial conferences, events, and summits.

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