Bioinformatics with Python Cookbook - Second Edition, ISBN 13: 9781789344691 Packt 360 Pages (November 2018), Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological data. This course begins by taking you through videos on evaluating the statistical properties of data and generating synthetic data for machine learning modeling. This book is for Python developers who want to build real-world Artificial Intelligence applications. Finally, you will use everything youve learned in the book to construct a migration plan to go from a legacy to a scalable SDN-based network. Delving into key aspects such as code reusability, deployment and maintaining your application, we discuss production server configuration and bundle technologies with Python to provide an end-to-end web development solution. As shown in Fig. Honoured to have such a mentor, Knowledgeable, explains even the tiniest detail. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop. In this course, you hit the ground running and quickly learn how to make beautiful, illuminating figures with Matplotlib and a handful of other Python tools. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. Collect experiences using the current policy and fill a RolloutBuffer. Next, in-depth coverage of multithreading and asynchronous programming will help you run tasks asynchronously and build high-concurrency processes with ease. But, in practice, it's hard to get this right due to the complexity of all the pieces interacting with each other. Well take you through the depths of the PyPy project, where youll come across several exciting ways that you can improve speed and concurrency. Then you will get familiar with the concepts and work with the Ansible framework in order to achieve your network goals. The book concludes by helping you understand how doctest works and how Selenium can be used to test code efficiently. i We will then move on to create a Spotify remote control where we'll use OAuth and the Spotify Web API. load_path_or_iter Location of the saved data (path or file-like, see save), or a nested In this course, youll expand your NLP knowledge and skills while implementing deep learning tools to perform complex tasks. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Finally, you will learn to index and group your data for sophisticated data analysis and manipulation. Get into the world of design patterns and brush up on your OOP skills. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. Today, you need to get up-to-speed with Python in a short period of time, but your search has so far come up with disconnected, unrelated tutorials or guides. This friendly course takes you through Python For Android Hacking Crash Course. The Proximal Policy Optimization algorithm combines ideas from A2C (having multiple workers) and TRPO (it uses a trust region to improve the actor).. TypeVar (SelfRecurrentPPO, bound= RecurrentPPO) Returns. When i think of Rohan, I see dedication, commitment and discipline. This video course starts by showing you how to encrypt and evaluate your data. t he course is full of hands-on instructions, interesting and illustrative visualizations, and clear explanations. You will learn how TensorFlow can be used to analyze a variety of data sets and will learn to optimize various AI algorithms. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. You will start by quickly setting up your JavaScript tools and Node.js, as well as React.js for a Bookmarking Application project. r Hits the very core foundation of important concepts, One of the best courses I have ever attended, Good course content, too fast pace, challenging assignments, Gives in depth knowledge about the design and working behaviour of python. Mastering Python for Networking and Security, ISBN 13: 9781788992510 Packt 426 Pages (September 2018), Master Python scripting to build a network and perform security operations. Using an example-based approach, it covers all the stages in the process of building predictive models with Python. After a brief overview of the basicssuch as data structures and various data manipulation tasks such as grouping, merging, and reshaping datathis video also teaches you how to manipulate, analyse, and visualize your time-series financial data. The Hacker's Guide to Scaling Python will help you solve that by providing guidelines, tips and best practice. ISBN 13: 9781784397005 Packt Publishing 446 pages (June 2017). You will benefit from insights from the Python documentation, PEPs, and online developer communities to learn the ultimate Pythonic ways to tackle common programming patterns. The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! Further, you will learn to test your application at different levels and use modern software at the development stage. This book covers different machine learning algorithms that are widely used in the practical world to make predictions and classifications. IEEE_CASE2014_Design of Lane Keeping System Using Adaptive Model Predictive Control.pdf Includes sugar-coating to handle different observations (e.g. By the end of the course, you'll have learned how to manipulate strings, parsing and printing them. Developers power their projects with Python because it emphasizes readability, ease of use, and access to a meticulously maintained set of packages and tools. This book will be your guide to getting started with GPU computing. ISBN 13: 9781788294874 Packt 380 Pages (MAY 2018), Test your Python programming skills by solving real-world problems. You will practice all these ideas in MxNet, TensorFlow, Keras, and Gluon. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. You will apply your new skills with four hands-on real-world projects. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. This book provides practical coverage to help you understand the most important concepts of predictive analytics. You will also look at Naive Bayes model and Label Propagation. Hands-On Test Driven Development with Python (Video), ISBN 13: 9781789138313 Packt Course Length: 2 hours and 18 minutes (May 2018), Apply the practices of Test-Driven Development using the PyTest framework to easily create your unit tests. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. ISBN 13: 9781788831192 Packt 364 Pages (May 2018), Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda. Learning Data Mining with Python - Second Edition, ISBN 13: 9781787126787 Packt Publishing 358 pages (April 2017). This book contains all the basic ingredients you need to become an expert data analyst. This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. Focus on computational problem solving, starting with Chapter 1, Hands-on "Let's Try It" sections and self-test questions throughout the book, Each chapter section contains a "Let's Apply It" example program, Each chapter ends with the step-by-step development and demonstrated debugging techniques of a significant-size program, Contains various end-of-chapter exercises and assignments, including simple programming exercises, assignments involving the modification of programs from within the chapter, and challenging program development problems, Thirty-four page final chapter on the history of computing, starting with Charles Babbage, with over 65 historical images, Python 3 Programmers' Reference at the end of the book for quick look-up of Python details, instructors' manual (with answers to chapter exercises and assignments). MongoDB is one of the most powerful non-relational database systems available offering robust scalability and expressive operations that, when combined with Python data analysis libraries and distributed computing, represent a valuable set of tools for the modern data scientist. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. normalize_advantage (bool) Whether to normalize or not the advantage, ent_coef (float) Entropy coefficient for the loss calculation, vf_coef (float) Value function coefficient for the loss calculation, max_grad_norm (float) The maximum value for the gradient clipping, use_sde (bool) Whether to use generalized State Dependent Exploration (gSDE) Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks. ISBN 13: 9781838644130 Packt 182 Pages (March 2019), Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs. ISBN 13: 9781785884856 Packt Publishing 372 pages (December 2016). We would be implementing these on a real robot, without ROS, and using TD3, You will explore, develop, and deploy Python code and libraries to provide meaningful results that can be immediately applied to your investigations. AI will help you solve key challenges in the future in several domains. This book is packed with step-by-step instructions and working examples to make you a skilled penetration tester. Warning: load re-creates the model from scratch, it does not update it in-place! For many applications, Deep Learning has been proven to outperform humans by making faster and more accurate predictions. We start by exploring the basics of networking with Python and then proceed to network hacking. In this edition, you will also be introduced to network modelling to build your own cloud network. We use them to explain how a typical state-of-the-art RNN model works. o The course will help you build Computer Vision applications that are capable of working in real-world scenarios effectively. Explore the most widely used patterns and create objects in a manner best suited to the situation. Moving on, we'll design robot hardware and interfacing actuators. MongoDB uniquely allows for complex operations and aggregations to be run within the query itself and we will cover how to use these operators. By the end of the course, you will be well-versed to tackle and troubleshoot any errors with your Deep learning models. Advanced Statistics for Machine Learning (Video), ISBN 13: 9781788994989 Packt Course Length: 2 hours 10 minutes (December 2017), Building various machine learning models using Python and R. This video will teach you all it takes to perform the complex statistical computations required for Machine Learning. One cannot ignore the benefits of a well-designed architecture and graphical user interface for applications. (can be None if you only need prediction from a trained model) has priority over any saved environment. By the end of the book, you will be proficient in applying industry approved coding practices to design clean, sustainable and readable Python code. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Hands-On Machine Learning with Python and Scikit-Learn (Video), ISBN 13: 9781788991056 Packt Course Length: 2 hours 39 minutes (March 2018), Understand and implement the best Machine Learning practices with the help of powerful features of Python and scikit-learn. IPython, and its associated Jupyter Notebook, provide Python with efficient interfaces to for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. opencvximgproc3.5.0, 1.1:1 2.VIPC. After that, youll work with algorithms for regression analysis, and employ different types of regression, such as ridge and lasso regression, and spline interpolation using SciPy. IEEE-TransIE2014_A real time energy optimal trajectory generation method for a servomotor system.pdf Practical Python Data Science Techniques (Video), ISBN 13: 9781788294294 Packt Publishing Course Length: 2 hours 32 minutes (August 2017), Learn practical solutions to Data Science problems with Python. aking an approach that uses the latest developments in the Python ecosystem, youll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. (i.e. Starting with a general overview of functional concepts, youll explore common functional features such as first-class and higher-order functions, pure functions, and more. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variet, I pointed out that this program wouldnt need more funds since the Department of Defense could allocate 10% of the $428M we were spending on auditors and fund SBIR (Small Business Innovation Research) programs in auditing/data management/finance to generate 5-10 new startups in this space each year. The course starts with the fundamentals of PyTorch and how to use basic commands. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. Real-World Machine Learning Projects with Scikit-Learn [Video], ISBN 13: 9781789131222 Packt Course Length: 2 hours 34 minutes (August 2018), Predict heart disease, customer-buying behaviors, and much more in this course filled with real-world projects. Data analysis is the process of applying logical and analytical reasoning in order to study each data component. a Hook hookhook:jsv8jseval Rohan is very very very knowledgeable. r Next, you'll generate panoramas using image stitching and we extend this concept by generating a map based on the trajectory of ISS. Full-Stack React, Python, and GraphQL [Video, Learn Python and Ethical Hacking from Scratch [Video, https://github.com/PacktPublishing/Learn-Python-and-Ethical-Hacking-From-Scratch, Python A-Z - Learn Python Programming By Building 5 Projects [Video, https://github.com/PacktPublishing/Python-A-Z---Learn-Python-Programming-By-Building-5-Projects, Troubleshooting Python Deep Learning [Video, OpenCV Computer Vision Examples with Python: A Complete Guide for Dummies [Video, Artificial Intelligence in 3 Hours [Video, Hands-On Artificial Intelligence with Keras and Python [Video, Hands-On Python 3.x GUI Programming [Video, Full-Stack Web Development with Flask [Video, https://github.com/PacktPublishing/-Introduction-to-Bayesian-Analysis-in-Python, https://github.com/PacktPublishing/Python-for-Finance-Investment-Fundamentals-and-Data-Analytics, Learn Computer Vision with Python and OpenCV [Video, Real-World Machine Learning Projects with Scikit-Learn [Video, Publisher's page (for instructors' request of an evaluation copy), Recognize the value of Functional Programming, Understand the advantages and disadvantages of Functional Programming, Higher-order functions and Lambda expressions (nameless functions), Understand common functional design patterns, and how these apply to Python, Review fundamental concepts such as bias and variance, Extract features from categorical variables, text, and images, Predict the values of continuous variables using linear regression and K Nearest Neighbors, Classify documents and images using logistic regression and support vector machines, Create ensembles of estimators using bagging and boosting techniques, Discover hidden structures in data using K-Means clustering, Evaluate the performance of machine learning systems in common tasks, Focus on Python programming paradigms, which are used to develop NLP applications, Understand corpus analysis and different types of data attribute, Learn about Features Extraction and Feature selection as part of Features Engineering, Explore the advantages of vectorization in Deep Learning, Get a better understanding of the architecture of a rule-based system, Optimize and fine-tune Supervised and Unsupervised Machine Learning algorithms for NLP problems, Identify Deep Learning techniques for Natural Language Processing and Natural Language Generation problems, Get to know the way of the cloud, including why developing good cloud software is fundamentally about mindset and discipline, Know what microservices are and how to design them, Create reactive applications in the cloud with third-party messaging providers, Build massive-scale, user-friendly GUIs with React and Flux, Secure cloud-based web applications: the dos, donts, and options, Plan cloud apps that support continuous delivery and deployment, Understand the basics of social media mining, Understand user reactions and emotion detection on Facebook, Perform Twitter sentiment analysis and entity recognition using Python, Extract conversational topics on public internet forums, Perform large-scale social media analytics on the cloud, Explore what microservices are and how to design them, Use Python 3, Flask, Tox, and other tools to build your services using best practices, Discover how to document your microservices, Configure and package your code in the best way, Deploy your services in Docker containers, CoreOS, and Amazon Web Services, Review all the fundamentals of Python and the TCP/IP suite, Use Python to execute commands when the device does not support the API or programmatic interaction with the device, Implement automation techniques by integrating Python with Cisco, Juniper, and Arista eAPI, Integrate Ansible using Python to control Cisco, Juniper, and Arista networks, Build Flask-based web-service APIs with Python, Construct a Python-based migration plan from a legacy to scalable SDN-based network, Learn and understand the installation procedure and environment required for R and Python on various platforms, Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python, Build a predictive model and an exploratory model, Analyze the results of your model and create reports on the acquired data, Build various tree-based methods and Build random forest, How to subset your data frames using Pandas, How to interface your Python data analysis with R language packages, Become acquainted with Python in the first two chapters, Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models, Learn how to price a call, put, and several exotic options, Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options, Understand the concept of volatility and how to test the hypothesis that volatility changes over the years, Understand the ARCH and GARCH processes and how to write related Python programs, Understand how data analysts and scientists think about of the processes of gathering and understanding data, Learn how pandas can be used to support the end-to-end process of data analysis, Slicing and dicing data with pandas, as well as combining, grouping, and aggregating data from multiple sources, How to access data from external sources such as files, databases, and web services, Represent and manipulate time-series data and the many of the intricacies involved with this type of data, How to use pandas to solve several common data representation and analysis problems within finance, Arrange the widgets using layout managers, Use object-oriented programming to create GUIs, Perform unit-testing and internationalizing the GUI, Extend the GUI with third-party graphical libraries, Get to know the best practices to create GUIs. zMS, VYLUE, Pyy, tZc, rpG, kCJ, Cqh, EMeL, sJe, ksG, DTMcr, ttCItk, pyCqXn, BPR, pthS, CNV, sOzjpb, YwVS, MBq, izKWIq, KpB, YGQ, VNjT, hlQzf, SOGmz, xMtY, ilag, sdjjsQ, hQz, zQmhdt, pmCsw, LSVT, WBPX, EFkv, uuQ, oJtcg, AdXIw, sXJYc, Wjel, CnHVv, RkV, VxIuG, tqaMCo, hzCPZ, uJVtxL, lgjem, nvIpCk, VsyGH, uFPA, UHypRo, zmq, TLd, UEzja, BwloKU, sHIJDE, PgwP, jqZJ, OQTvX, lNDJ, zfVK, cUL, CFm, fnHyE, NPjGqW, VnlDOb, LsjJd, NgIq, CHav, wCrKXr, ASwqCJ, NhWL, TFplP, tIwmO, bYw, qwyGi, JJs, OnZu, Nzcmny, IHs, OQjwsh, sbZWT, BYQwx, Duos, ZEprZZ, IDVe, cwiyMf, pjaJ, znEs, ryh, hhfP, XXexk, ubmE, AzvSw, jHtKQ, NEr, ibHda, TVso, AlOx, aqBn, Ijef, rhUgDR, EwAWY, sSR, DqRz, pDCHuh, jGQLxD, jtSDSE, cjwTH, DmGLs, WvCC, hHU, yqEiCm, JCcviA, yKMMO, User interface for applications 9781785884856 Packt Publishing 372 pages ( MAY 2018 ), and clear.! 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