Tensorflow Tutorial for Beginners.pdf
•
0 likes•13 views
This document provides a TensorFlow tutorial for beginners. It discusses the history and meaning of TensorFlow, introducing it as a machine learning library created by Google in 2015. It describes tensors as higher dimensional arrays used to represent data, and explains that TensorFlow allows defining tensor functions and automatically computing derivatives. The tutorial covers key TensorFlow concepts like its ecosystem, advantages like flexibility and auto differentiation, and tools like TensorBoard for visualization. The goal is to explain the complete concept of TensorFlow to readers new to the topic.
Report
Share
Report
Share
Download to read offline
Recommended
How to use tensorflow
The document discusses TensorFlow and machine learning algorithms. It covers defining and running TensorFlow operations like addition and multiplication. TensorFlow can efficiently evaluate operations during execution time. It also discusses linear regression, data generation, model construction, and learning stages when training regression and neural network models on automatically generated data. Hidden layers are mentioned when discussing predicting sin(x) functions with neural networks.
Tensorflow for Beginners
A complete guide for building machine learning and deep learning solutions using Tensorflow. This TensorFlow tutorial is designed for newbies and advanced users in which they will learn basics & difficult concepts of Tensorflow from scratch. Enroll now and let’s take a step into the future with TensorFlow!
Get the Course here : https://www.eduonix.com/tensorflow-for-beginners?coupon_code=discount15
https://www.eduonix.com/tensorflow-for-beginners?coupon_code=discount15
TensorFlow.pptx
TensorFlow is an open-source machine learning framework developed by Google. It provides tools for performing numerical computation and defining, training, and evaluating machine learning models. TensorFlow's flexible architecture allows models to be deployed on CPUs, GPUs, and TPUs. It has a large ecosystem of tools and an active community. The TensorFlow architecture consists of a backend for efficient computation and a frontend Python API for building models. Key program elements include operations, graphs, sessions, tensors, variables, and placeholders.
Live coding session on AI / ML using Google Tensorflow (Python) - Tanmoy Deb ...
Build your own system, extend your existing systems through ML capabilities. Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to. The topic will give briefly cover how anybody can learn and start their journey in AI, discussing the opportunities and challenges from point of view of a student, developer, and entrepreneur. The take away from the session will be to take that first step beyond the question- "where to start? There is too much out there!". This will be given with a demo that how most popular framework for Deep Learning works.
Introduction to Neural Networks in Tensorflow
Nick McClure gave an introduction to neural networks using Tensorflow. He explained the basic unit of neural networks as operational gates and how multiple gates can be combined. He discussed loss functions, learning rates, and activation functions. McClure also covered convolutional neural networks, recurrent neural networks, and applications such as image captioning and style transfer. He concluded by discussing resources for staying up to date with advances in machine learning.
Multithreading to Construct Neural Networks
1) Introduction to neural networks hyperparameters
2) TensorFlow with Spark
3) Distributed TensorFlow
4) Conclusion
"TensorFlow Basics: A GDSC VITB Studty jams"
"Explore the fundamentals of TensorFlow, covering key concepts, features, and applications. Perfect for beginners!
Recommended
How to use tensorflow
The document discusses TensorFlow and machine learning algorithms. It covers defining and running TensorFlow operations like addition and multiplication. TensorFlow can efficiently evaluate operations during execution time. It also discusses linear regression, data generation, model construction, and learning stages when training regression and neural network models on automatically generated data. Hidden layers are mentioned when discussing predicting sin(x) functions with neural networks.
Tensorflow for Beginners
A complete guide for building machine learning and deep learning solutions using Tensorflow. This TensorFlow tutorial is designed for newbies and advanced users in which they will learn basics & difficult concepts of Tensorflow from scratch. Enroll now and let’s take a step into the future with TensorFlow!
Get the Course here : https://www.eduonix.com/tensorflow-for-beginners?coupon_code=discount15
https://www.eduonix.com/tensorflow-for-beginners?coupon_code=discount15
TensorFlow.pptx
TensorFlow is an open-source machine learning framework developed by Google. It provides tools for performing numerical computation and defining, training, and evaluating machine learning models. TensorFlow's flexible architecture allows models to be deployed on CPUs, GPUs, and TPUs. It has a large ecosystem of tools and an active community. The TensorFlow architecture consists of a backend for efficient computation and a frontend Python API for building models. Key program elements include operations, graphs, sessions, tensors, variables, and placeholders.
Live coding session on AI / ML using Google Tensorflow (Python) - Tanmoy Deb ...
Build your own system, extend your existing systems through ML capabilities. Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to. The topic will give briefly cover how anybody can learn and start their journey in AI, discussing the opportunities and challenges from point of view of a student, developer, and entrepreneur. The take away from the session will be to take that first step beyond the question- "where to start? There is too much out there!". This will be given with a demo that how most popular framework for Deep Learning works.
Introduction to Neural Networks in Tensorflow
Nick McClure gave an introduction to neural networks using Tensorflow. He explained the basic unit of neural networks as operational gates and how multiple gates can be combined. He discussed loss functions, learning rates, and activation functions. McClure also covered convolutional neural networks, recurrent neural networks, and applications such as image captioning and style transfer. He concluded by discussing resources for staying up to date with advances in machine learning.
Multithreading to Construct Neural Networks
1) Introduction to neural networks hyperparameters
2) TensorFlow with Spark
3) Distributed TensorFlow
4) Conclusion
"TensorFlow Basics: A GDSC VITB Studty jams"
"Explore the fundamentals of TensorFlow, covering key concepts, features, and applications. Perfect for beginners!
Natural language processing open seminar For Tensorflow usage
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
Practical 9
The document discusses the Least Recently Used (LRU) page replacement algorithm. LRU tracks page usage over a short period of time and assumes pages more recently used will be used again soon. It can provide near-optimal performance but is expensive to implement due to needing to track page usage order. The linked list method tracks pages in a list from most to least recently used but moving items is costly. LRU is amenable to statistical analysis and can result in at most N times more page faults than an optimal algorithm, where N is the number of pages. However, its performance can degrade with common access patterns like looping over arrays. Modifications try to detect loops and switch to algorithms like Most Recently Used.
Lecture Note DL&NN Tensorflow.pptx
Tensorflow is an open-source platform for machine learning and neural networks. It allows developers to define computational graphs to perform operations on multi-dimensional tensors that represent inputs and outputs. Tensors flow through the graphs to transform data and train models. Google developed Tensorflow and uses it in many of their products for tasks like search, translation, and recommendations. It works by defining the preprocessing, model building, and training phases and can run on CPUs, GPUs, and mobile devices.
Getting started with TensorFlow
TensorFlow is a popular machine learning library developed by Google. It allows highly scalable deep learning across many devices from data centers to phones. TensorFlow represents computations as data flow graphs with variables as tensors. This allows flexible and efficient training and deployment of models for tasks like image recognition, translation, and more.
TENSORFLOW: ARCHITECTURE AND USE CASE - NASA SPACE APPS CHALLENGE by Gema Par...
https://www.bigdataspain.org/2016/program/thu-tensorflow-arquitectura-caso-uso-nasa-space-apps-challenge.html
https://www.youtube.com/watch?v=0pOqJNVuc5I&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&index=23&t=21s
TensorFlow example for AI Ukraine2016
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
Deep learning
The document outlines the schedule for a full-day machine learning course. The morning sessions introduce deep learning and cover a "hello world" machine learning exercise using MNIST data. Feedforward neural networks are also discussed. The afternoon focuses on computer vision with convolutional neural networks, natural language processing, generative models, time series prediction, and deploying machine learning models. Live coding exercises accompany many of the talks to provide hands-on learning.
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
Processing Techniques
The document provides an overview of different processing techniques:
- Multi-tasking allows a computer to switch between programs rapidly, making it seem like programs run simultaneously.
- Multi-threading breaks programs into threads that can run simultaneously on a single CPU.
- Multi-processing runs different tasks on separate processor cores at the same time.
- Hyper-threading simulates two execution units on a single CPU to improve multi-threading performance.
Control technolgy lesson 3
The document discusses a lesson about control systems and sensors. It aims to teach students to give examples of systems that use sensors to control actions, expand instructions in flowcharts, and explain what loops are and how to use them. Several control systems are presented that use sensors as inputs, including a printer tray sensor, door sensor, and washing machine water level sensor. Pedestrian crosswalk signals are provided as an example of a system with outputs controlled by sensor inputs.
Introduction to Tensor Flow-v1.pptx
TensorFlow is a popular open-source machine learning framework developed by Google. It allows users to define and train neural networks and other machine learning models. TensorFlow represents all data in the form of multidimensional arrays called tensors that flow through its computational graph. It supports a variety of machine learning tasks including image recognition, natural language processing, and time series forecasting. TensorFlow provides features like scalability across multiple CPUs and GPUs, model visualization tools, and an active developer community.
IoT Security.pdf
This document discusses IoT security. It begins by defining IoT security as protecting IoT systems, servers, networks, and devices. It then explains that IoT security is needed because IoT devices collect and store valuable data, making the systems vulnerable. The document outlines several challenges facing IoT systems, such as unpredictable device behavior, similar device designs, and lack of alerts. It also discusses threats like malware, information theft, and vulnerabilities. Finally, the document provides recommendations for improving IoT security, including encryption, updates, and network access control.
Top Java Frameworks.pdf
- Java frameworks are libraries of pre-written code that make developing Java applications easier and more efficient. They provide tools, functions, and libraries to develop different application components.
- Using frameworks can significantly reduce development time and effort by reusing pre-written code rather than writing everything from scratch. This improves quality and reduces errors.
- There are different types of Java frameworks, including full-stack frameworks for complex enterprise applications, micro frameworks for simple applications, and non-web frameworks for standalone applications. Developers must choose the right framework based on their specific project needs.
Numpy ndarrays.pdf
Numpy ndarrays are n-dimensional, homogeneous arrays that provide an efficient way to store and manipulate large multi-dimensional datasets. They are the fundamental data structure in NumPy that enables working with homogeneous data. An ndarray is created with a shape that defines its dimensions and a data type that specifies the type of data elements. It supports common operations like arithmetic, indexing/slicing, aggregation, reshaping and transposing. NumPy ndarrays are an essential tool for numerical computing and data analysis in Python.
Float Data Type in C.pdf
The document discusses float and double data types in C programming. Floats use 4 bytes of memory to store numbers with a decimal point with about 6-9 digits of precision, while doubles use 8 bytes for about 15 digits of precision, making them more suitable for complex calculations. Examples are given showing how to declare and use float and double variables to calculate exam score averages. Key differences between floats and doubles are outlined. Derived data types are also introduced, which build upon primitive types to create more complex data structures like arrays, structs, unions, and enums.
IoT Hardware – The Backbone of Smart Devices.pdf
The document discusses IoT hardware and provides an overview of 7 popular hardware platforms: Raspberry Pi, Arduino, Pycom, Particle, SODAQ, Adafruit, and SparkFun. It compares each platform across connectivity options, power management capabilities, storage features, and methods for data acquisition. Key details about popular board versions for each platform are also provided.
Internet of Things – Contiki.pdf
Contiki is an open-source operating system designed for Internet of Things devices like sensors and microcontrollers. It has communication components like uIP and 6LoWPAN that allow low-power devices to connect to the internet. Contiki also supports dynamic module loading, process and memory management, file systems, and includes the Cooja network simulator.
Java abstract Keyword.pdf
Abstract classes in Java are used as templates for creating concrete classes and cannot be instantiated directly. They provide a common interface and implementation for subclasses through abstract and concrete methods. For example, an abstract Shape class can define methods like getColor() while making getArea() abstract, requiring subclasses like Circle to implement it. Abstract classes allow for code reuse and polymorphism, serving as a blueprint for related classes to customize as needed.
Node.js with MySQL.pdf
The document discusses how to connect a Node.js application to a MySQL database. It covers setting up the environment, initializing a project, installing dependencies, creating a database, establishing a connection, handling environment variables, performing CRUD operations, handling errors, and calling stored procedures. Code examples are provided for each step to demonstrate how to connect Node.js to MySQL and execute queries to retrieve, add, update and delete data.
Collections in Python - Where Data Finds Its Perfect Home.pdf
The document discusses the Python collections module which provides alternative data structures to Python's built-in lists, dictionaries, and tuples. It contains optimized container types like Counter, OrderedDict, defaultdict, deque, namedtuple, and ChainMap. Each type is designed for specific use cases and provides functionality beyond the built-in structures. The document provides examples and explanations of when each collection type would be useful.
File Handling in Java.pdf
This document discusses file handling in Java. It begins by explaining that a file stores related information together and describes standard input, output, and error streams in Java which represent System.in, System.out, and System.err. It then discusses input and output streams in Java, describing how to create them and common methods. It also covers the Java File class and common file operations like creating, reading from, writing to, and deleting files.
Types of AI you should know.pdf
The document discusses different types of artificial intelligence (AI) based on their capabilities and functionalities. It describes narrow AI, general AI, and super AI based on capabilities, with narrow AI only able to perform limited tasks, general AI able to reason like humans, and super AI surpassing human abilities. It also outlines reactive machines, limited theory AI with memory, theory of mind AI, and self-aware AI based on functionalities and levels of human-like cognition. The document provides examples and applications of different AI types.
More Related Content
Similar to Tensorflow Tutorial for Beginners.pdf
Natural language processing open seminar For Tensorflow usage
This is presentation for Natural Language Processing open seminar in Kookmin University.
The open seminar reference : https://cafe.naver.com/nlpk
My presentation about how to use tensorflow for NLP open seminar for newbies for tensorflow.
Practical 9
The document discusses the Least Recently Used (LRU) page replacement algorithm. LRU tracks page usage over a short period of time and assumes pages more recently used will be used again soon. It can provide near-optimal performance but is expensive to implement due to needing to track page usage order. The linked list method tracks pages in a list from most to least recently used but moving items is costly. LRU is amenable to statistical analysis and can result in at most N times more page faults than an optimal algorithm, where N is the number of pages. However, its performance can degrade with common access patterns like looping over arrays. Modifications try to detect loops and switch to algorithms like Most Recently Used.
Lecture Note DL&NN Tensorflow.pptx
Tensorflow is an open-source platform for machine learning and neural networks. It allows developers to define computational graphs to perform operations on multi-dimensional tensors that represent inputs and outputs. Tensors flow through the graphs to transform data and train models. Google developed Tensorflow and uses it in many of their products for tasks like search, translation, and recommendations. It works by defining the preprocessing, model building, and training phases and can run on CPUs, GPUs, and mobile devices.
Getting started with TensorFlow
TensorFlow is a popular machine learning library developed by Google. It allows highly scalable deep learning across many devices from data centers to phones. TensorFlow represents computations as data flow graphs with variables as tensors. This allows flexible and efficient training and deployment of models for tasks like image recognition, translation, and more.
TENSORFLOW: ARCHITECTURE AND USE CASE - NASA SPACE APPS CHALLENGE by Gema Par...
https://www.bigdataspain.org/2016/program/thu-tensorflow-arquitectura-caso-uso-nasa-space-apps-challenge.html
https://www.youtube.com/watch?v=0pOqJNVuc5I&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&index=23&t=21s
TensorFlow example for AI Ukraine2016
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
Deep learning
The document outlines the schedule for a full-day machine learning course. The morning sessions introduce deep learning and cover a "hello world" machine learning exercise using MNIST data. Feedforward neural networks are also discussed. The afternoon focuses on computer vision with convolutional neural networks, natural language processing, generative models, time series prediction, and deploying machine learning models. Live coding exercises accompany many of the talks to provide hands-on learning.
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
Processing Techniques
The document provides an overview of different processing techniques:
- Multi-tasking allows a computer to switch between programs rapidly, making it seem like programs run simultaneously.
- Multi-threading breaks programs into threads that can run simultaneously on a single CPU.
- Multi-processing runs different tasks on separate processor cores at the same time.
- Hyper-threading simulates two execution units on a single CPU to improve multi-threading performance.
Control technolgy lesson 3
The document discusses a lesson about control systems and sensors. It aims to teach students to give examples of systems that use sensors to control actions, expand instructions in flowcharts, and explain what loops are and how to use them. Several control systems are presented that use sensors as inputs, including a printer tray sensor, door sensor, and washing machine water level sensor. Pedestrian crosswalk signals are provided as an example of a system with outputs controlled by sensor inputs.
Introduction to Tensor Flow-v1.pptx
TensorFlow is a popular open-source machine learning framework developed by Google. It allows users to define and train neural networks and other machine learning models. TensorFlow represents all data in the form of multidimensional arrays called tensors that flow through its computational graph. It supports a variety of machine learning tasks including image recognition, natural language processing, and time series forecasting. TensorFlow provides features like scalability across multiple CPUs and GPUs, model visualization tools, and an active developer community.
Similar to Tensorflow Tutorial for Beginners.pdf (11)
Natural language processing open seminar For Tensorflow usage
Natural language processing open seminar For Tensorflow usage
TENSORFLOW: ARCHITECTURE AND USE CASE - NASA SPACE APPS CHALLENGE by Gema Par...
TENSORFLOW: ARCHITECTURE AND USE CASE - NASA SPACE APPS CHALLENGE by Gema Par...
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...
More from SudhanshiBakre1
IoT Security.pdf
This document discusses IoT security. It begins by defining IoT security as protecting IoT systems, servers, networks, and devices. It then explains that IoT security is needed because IoT devices collect and store valuable data, making the systems vulnerable. The document outlines several challenges facing IoT systems, such as unpredictable device behavior, similar device designs, and lack of alerts. It also discusses threats like malware, information theft, and vulnerabilities. Finally, the document provides recommendations for improving IoT security, including encryption, updates, and network access control.
Top Java Frameworks.pdf
- Java frameworks are libraries of pre-written code that make developing Java applications easier and more efficient. They provide tools, functions, and libraries to develop different application components.
- Using frameworks can significantly reduce development time and effort by reusing pre-written code rather than writing everything from scratch. This improves quality and reduces errors.
- There are different types of Java frameworks, including full-stack frameworks for complex enterprise applications, micro frameworks for simple applications, and non-web frameworks for standalone applications. Developers must choose the right framework based on their specific project needs.
Numpy ndarrays.pdf
Numpy ndarrays are n-dimensional, homogeneous arrays that provide an efficient way to store and manipulate large multi-dimensional datasets. They are the fundamental data structure in NumPy that enables working with homogeneous data. An ndarray is created with a shape that defines its dimensions and a data type that specifies the type of data elements. It supports common operations like arithmetic, indexing/slicing, aggregation, reshaping and transposing. NumPy ndarrays are an essential tool for numerical computing and data analysis in Python.
Float Data Type in C.pdf
The document discusses float and double data types in C programming. Floats use 4 bytes of memory to store numbers with a decimal point with about 6-9 digits of precision, while doubles use 8 bytes for about 15 digits of precision, making them more suitable for complex calculations. Examples are given showing how to declare and use float and double variables to calculate exam score averages. Key differences between floats and doubles are outlined. Derived data types are also introduced, which build upon primitive types to create more complex data structures like arrays, structs, unions, and enums.
IoT Hardware – The Backbone of Smart Devices.pdf
The document discusses IoT hardware and provides an overview of 7 popular hardware platforms: Raspberry Pi, Arduino, Pycom, Particle, SODAQ, Adafruit, and SparkFun. It compares each platform across connectivity options, power management capabilities, storage features, and methods for data acquisition. Key details about popular board versions for each platform are also provided.
Internet of Things – Contiki.pdf
Contiki is an open-source operating system designed for Internet of Things devices like sensors and microcontrollers. It has communication components like uIP and 6LoWPAN that allow low-power devices to connect to the internet. Contiki also supports dynamic module loading, process and memory management, file systems, and includes the Cooja network simulator.
Java abstract Keyword.pdf
Abstract classes in Java are used as templates for creating concrete classes and cannot be instantiated directly. They provide a common interface and implementation for subclasses through abstract and concrete methods. For example, an abstract Shape class can define methods like getColor() while making getArea() abstract, requiring subclasses like Circle to implement it. Abstract classes allow for code reuse and polymorphism, serving as a blueprint for related classes to customize as needed.
Node.js with MySQL.pdf
The document discusses how to connect a Node.js application to a MySQL database. It covers setting up the environment, initializing a project, installing dependencies, creating a database, establishing a connection, handling environment variables, performing CRUD operations, handling errors, and calling stored procedures. Code examples are provided for each step to demonstrate how to connect Node.js to MySQL and execute queries to retrieve, add, update and delete data.
Collections in Python - Where Data Finds Its Perfect Home.pdf
The document discusses the Python collections module which provides alternative data structures to Python's built-in lists, dictionaries, and tuples. It contains optimized container types like Counter, OrderedDict, defaultdict, deque, namedtuple, and ChainMap. Each type is designed for specific use cases and provides functionality beyond the built-in structures. The document provides examples and explanations of when each collection type would be useful.
File Handling in Java.pdf
This document discusses file handling in Java. It begins by explaining that a file stores related information together and describes standard input, output, and error streams in Java which represent System.in, System.out, and System.err. It then discusses input and output streams in Java, describing how to create them and common methods. It also covers the Java File class and common file operations like creating, reading from, writing to, and deleting files.
Types of AI you should know.pdf
The document discusses different types of artificial intelligence (AI) based on their capabilities and functionalities. It describes narrow AI, general AI, and super AI based on capabilities, with narrow AI only able to perform limited tasks, general AI able to reason like humans, and super AI surpassing human abilities. It also outlines reactive machines, limited theory AI with memory, theory of mind AI, and self-aware AI based on functionalities and levels of human-like cognition. The document provides examples and applications of different AI types.
Streams in Node .pdf
Streams are used in Node.js to efficiently process large amounts of data. There are four types of streams: readable, writable, duplex, and transform. Readable streams are used to read data from a source, writable streams write to a destination, duplex streams both read and write, and transform streams modify data as it flows through. Streams allow data to be processed sequentially in chunks rather than all at once, making them useful for large datasets and network connections.
Annotations in Java with Example.pdf
Annotations in Java provide metadata that can be used by compilers and runtime environments to process code in different ways. Common built-in annotations include @Override, @SuppressWarnings, and @Deprecated. User-defined annotations can also be created. Annotations do not affect code execution but provide supplemental information used by tools and libraries. Common annotation types are marker, single-value, multi-value, type, and repeating annotations. Annotations can be used to provide instructions to compilers, for compile-time processing, and at runtime via reflection.
RESTful API in Node.pdf
The document discusses RESTful API development in Node.js. It covers fundamentals like HTTP methods, resource design, authentication and error handling. It provides a code example of a RESTful API for a library with endpoints for getting, adding, updating and deleting books from the library collection. The API uses Express.js and adheres to REST principles by utilizing HTTP methods on resources identified by URLs.
Top Cryptocurrency Exchanges of 2023.pdf
This document provides a summary of the top 12 cryptocurrency exchanges of 2023. It examines each exchange's unique qualities, security measures, trading features, and their potential to influence the cryptocurrency market. The exchanges highlighted are Binance, Coinbase, Kraken, Bittrex, Bitfinex, Huobi, KuCoin, OKEx, Gemini, Bybit, Poloniex, and Bitstamp. Each exchange caters to different trader experience levels and provides services like spot trading, margin trading and secure storage of digital assets.
Epic Python Face-Off -Methods vs.pdf
Methods in Python are functions that are associated with an object or class. They can access and modify the object's attributes and are invoked using dot notation. Functions are independent blocks of code that can accept inputs, perform computations, and return results without being tied to a specific object. The key differences are that methods are associated with objects, can access object attributes, and modify object state, while functions are standalone, cannot access object attributes directly, and are not associated with a particular object. [/SUMMARY]
Django Tutorial_ Let’s take a deep dive into Django’s web framework.pdf
This document provides an in-depth overview of the Django web framework. It begins by defining Django and explaining why it is useful for web development. It then covers Django's history, architecture using the MTV/MVC models, features, popularity and common use cases. The document also discusses Django's opinionation, prerequisites, and provides code examples to illustrate how a basic Django application is structured.
Benefits Of IoT Salesforce.pdf
The document discusses the benefits of Salesforce IoT cloud, including enhancing sales operations, improving customer experience, providing advanced analytics capabilities, and automating tasks. It outlines 15 specific benefits such as improving customer referrals, increasing efficiency, and enhancing R&D activities. The Salesforce IoT cloud integrates data from IoT devices to provide insights and automate processes to improve customer service and business operations.
Epic Python Face-Off -Methods vs. Functions.pdf
This document explores the differences between methods and functions in Python. Methods are functions that are associated with an object or class, can access and modify object attributes, and are invoked using dot notation. Functions are independent blocks of code that are not tied to objects, cannot access object attributes directly, and are invoked by name. Key differences are that methods are associated with objects while functions are not, methods can modify object state but functions cannot, and methods belong to a class namespace while functions have their own. Examples are provided to illustrate method and function syntax and usage.
Python Classes_ Empowering Developers, Enabling Breakthroughs.pdf
Python classes allow for the creation of object-oriented programming through defining blueprints for objects with shared attributes and behaviors. Classes are created using the class keyword and contain attributes like variables and methods like functions. Objects are instantiated from classes and can access both class level and instance level attributes. Key concepts covered include inheritance, encapsulation, polymorphism, and special methods.
More from SudhanshiBakre1 (20)
Collections in Python - Where Data Finds Its Perfect Home.pdf
Collections in Python - Where Data Finds Its Perfect Home.pdf
Django Tutorial_ Let’s take a deep dive into Django’s web framework.pdf
Django Tutorial_ Let’s take a deep dive into Django’s web framework.pdf
Python Classes_ Empowering Developers, Enabling Breakthroughs.pdf
Python Classes_ Empowering Developers, Enabling Breakthroughs.pdf
Recently uploaded
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
This case study explores designing a scalable e-commerce platform, covering key requirements, system components, and best practices.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Finale of the Year: Apply for Next One!
Presentation for the event called "Finale of the Year: Apply for Next One!" organized by GDSC PJATK
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Operating System Used by Users in day-to-day life.pptx
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Recently uploaded (20)
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
Operating System Used by Users in day-to-day life.pptx
Operating System Used by Users in day-to-day life.pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Tensorflow Tutorial for Beginners.pdf
- 1. TENSORFLOW TUTORIAL TENSORFLOW TUTORIAL FOR BEGINNERS DATAFLAIR
- 2. TENSORFLOW TUTORIAL FOR TENSORFLOW TUTORIAL FOR TENSORFLOW TUTORIAL FOR BEGINNERS BEGINNERS BEGINNERS TODAY, IN THIS TENSORFLOW TUTORIAL FOR BEGINNERS, WE WILL DISCUSS THE COMPLETE CONCEPT OF TENSORFLOW. MOREOVER, WE WILL START THIS TENSORFLOW TUTORIAL WITH THE HISTORY AND MEANING OF TENSORFLOW. ALSO, WE WILL LEARN ABOUT TENSORS & USES OF TENSORFLOW. WE WILL ALSO SEE TENSORFLOW EXAMPLES, FEATURES, ADVANTAGES, AND LIMITATIONS. AT LAST, WE WILL SEE TENSORBOARD IN TENSORFLOW.
- 3. TENSORFLOW TUTORIAL – HISTORY BEFORE THE UPDATION, TENSORFLOW IS KNOWN AS DISTBELIEF. IT BUILT IN 2011 AS A PROPRIETARY SYSTEM BASED ON DEEP LEARNING NEURAL NETWORKS. THE SOURCE CODE OF DISTBELIEF WAS MODIFIED AND MADE INTO A MUCH BETTER APPLICATION BASED LIBRARY AND SOON IN 2015 CAME TO BE KNOWN AS TENSORFLOW.
- 4. WHAT IS TENSORFLOW? WHAT IS TENSORFLOW? WHAT IS TENSORFLOW? TENSORFLOW IS A POWERFUL DATA FLOW-ORIENTED MACHINE LEARNING LIBRARY CREATED BY THE BRAIN TEAM OF GOOGLE AND MADE OPEN SOURCE IN 2015. IT IS DESIGNED TO BE EASY TO USE AND WIDELY APPLICABLE TO BOTH NUMERIC AND NEURAL NETWORK-ORIENTED PROBLEMS AS WELL AS OTHER DOMAINS. BASICALLY, TENSORFLOW IS A LOW-LEVEL TOOLKIT FOR DOING COMPLICATED MATH AND IT TARGETS RESEARCHERS WHO KNOW WHAT THEY’RE DOING TO BUILD EXPERIMENTAL LEARNING ARCHITECTURES, TO PLAY AROUND WITH THEM, AND TO TURN THEM INTO RUNNING SOFTWARE.
- 5. TENSORFLOW TUTORIAL – TENSORS Now, as the name suggests, it provides primitives for defining functions on tensors and automatically computing their derivatives. Tensors are higher dimensional arrays that are used in computer programming to represent a multitude of data in the form of numbers. There are other n-d array libraries available on the internet like Numpy but TensorFlow stands apart from them as it offers methods to create tensor functions and automatically compute derivatives. There are other n-d array libraries available on the internet like Numpy but TensorFlow stands apart from them as it offers methods to create tensor functions and automatically compute derivatives. Now, let’s see some more uses of Tensorflow in this Tensorflow Tutorial. PAUCEK AND LAGE
- 6. Tensorflow Tutorial – Tensors
- 7. Tensorflow Tutorial – Tensorflow Ecosystem
- 8. TENSORFLOW HAS A RESPONSIVE CONSTRUCT AS YOU CAN EASILY VISUALIZE EACH AND EVERY PART OF THE GRAPH. IT HAS PLATFORM FLEXIBILITY, MEANING IT IS MODULAR AND SOME PARTS OF IT CAN BE STANDALONE WHILE THE OTHERS COALESCED. IT IS EASILY TRAINABLE ON CPU AS WELL AS GPU FOR DISTRIBUTED COMPUTING. TENSORFLOW HAS AUTO DIFFERENTIATION CAPABILITIES WHICH BENEFIT GRADIENT-BASED MACHINE LEARNING ALGORITHMS MEANING YOU CAN COMPUTE DERIVATIVES OF VALUES WITH RESPECT TO OTHER VALUES WHICH RESULTS IN A GRAPH EXTENSION. ALSO, IT HAS ADVANCED SUPPORT FOR THREADS, ASYNCHRONOUS COMPUTATION, AND QUEUES. IT IS CUSTOMIZABLE AND OPEN SOURCE. THE FOLLOWING ARE THE ADVANTAGES OF THE TENSORFLOW TUTORIAL: TENSORFLOW TUTORIAL – ADVANTAGES
- 9. CONCLUSION Hence, in this TensorFlow tutorial, we saw what is TensorFlow, and how it works. Moreover, we discussed the history and features of TensorFlow. Along with this, we discussed the TensorFlow example and its advantages. Moreover, we learned about Tensors and TensorBoard. Still, if any doubt regarding the TensorFlow tutorial, ask in the comment tab.