From Zero To Ai Hero Create Neural Networks With Tensorflow

What you'll learn
Understand the fundamentals of artificial intelligence and machine learning.
Set up the TensorFlow environment to begin building neural networks.
Explore the reasons behind learning TensorFlow and its importance in the field of machine learning.
Gain familiarity with AI and machine learning concepts, including supervised learning, unsupervised learning, and neural networks.
Apply the machine learning workflow using TensorFlow and Python, from data preprocessing to model evaluation.
Comprehend the inner workings of neural networks, including their architecture, activation functions, and backpropagation algorithm.
Build and train their first neural network using TensorFlow, implementing various layers, optimization techniques, and loss functions.
Monitor and improve the performance of neural networks through techniques such as regularization and hyperparameter tuning.
Learn techniques for deploying trained neural networks, including model conversion and deployment options.
Receive practical tips and best practices for working with TensorFlow and neural networks.
Gain hands-on experience through coding exercises and projects, allowing students to apply their knowledge and build their own neural network models.
Obtain a comprehensive understanding of the entire process of building, training, and deploying neural networks with TensorFlow.
Acquire the confidence to continue exploring and experimenting with neural networks for various real-world applications.
Requirements
No prior experience with TensorFlow required.
Willingness to learn and explore.
Access to a computer with a stable internet connection.
Basic programming knowledge (Python recommended).
Description
Get ready to build, train, and deploy intelligent systems that can revolutionize various industries and solve complex problems.Are you fascinated by the world of artificial intelligence and machine learning? Do you want to unlock the power of neural networks and learn how to build intelligent systems? Look no further! Join our comprehensive course on building, training, and deploying neural networks with TensorFlow, the leading framework for deep learning in Python.In this course, you will embark on an exciting journey that will take you from the fundamentals of TensorFlow to creating your very own neural networks. Whether you are a beginner in machine learning or an experienced developer looking to expand your skillset, this course is designed to cater to learners of all levels. No prior knowledge of TensorFlow or deep learning is required!Why choose this course?Practical approach: We believe in hands-on learning, and this course is packed with real-world examples and projects that will reinforce your understanding of TensorFlow and neural networks.User-friendly structure: Our course is organized into ten engaging lessons, allowing you to progress step-by-step at your own pace. Each lesson builds upon the previous one, ensuring a seamless learning experience.Python-powered: Python is the language of choice for machine learning, and this course leverages its simplicity and versatility to teach you the concepts and techniques required to build powerful neural networks.What will you learn in comprehensive and user-friendly course?Course Overview: Get acquainted with the course structure and objectives, setting the stage for your deep learning journey.Why Learn TensorFlow: Discover the significance of TensorFlow in the world of AI and machine learning, and learn why it's the go-to framework for neural networks.Setting up the TensorFlow Environment: Learn how to install and configure TensorFlow on your machine, ensuring a smooth development experience.AI and Machine Learning Concepts: Build a strong foundation by exploring essential concepts in artificial intelligence and machine learning.Applying the Machine Learning Workflow with TensorFlow and Python: Understand the end-to-end process of building machine learning models using TensorFlow and Python, from data preparation to model evaluation.Understanding Neural Networks: Dive deep into the world of neural networks, understanding their structure, components, and functioning.Building and Training Your First Neural Network: Roll up your sleeves and start building! Learn how to design and train your very own neural network using TensorFlow.Monitoring and Improving Neural Network Performance: Discover techniques to monitor and evaluate the performance of your neural network, and explore strategies to enhance its accuracy and efficiency.Deploying Your Neural Network: Learn how to deploy your trained neural network into production environments, making it available for real-world applications.Final Words: Wrap up the course with a recap of the key concepts and insights gained, and explore avenues for further exploration and growth in the field of deep learning.Example Projects:House Price Prediction: Harness the power of neural networks to predict house prices based on their sizes, gaining insights into regression tasks.Cloth Type Identification: Develop a neural network that can identify different types of clothing items from images, opening doors to image classification techniques.Don't miss this golden opportunity to unlock the power of artificial intelligence and machine learning. Enroll now and take your first step towards becoming a TensorFlow hero!
Overview
Section 1: Introduction
Lecture 1 Course Overview
Lecture 2 Project Files & Source Code
Lecture 3 Important Note About Video Playback Controls
Section 2: Why learn TenserFlow
Lecture 4 What Is TensorFlow
Lecture 5 Why TensorFlow
Lecture 6 TensorFlow Tools and Languages
Lecture 7 Required Skills
Lecture 8 Course Structure
Section 3: Setting up the TensorFlow Environment
Lecture 9 TensorFlow Development Environment
Lecture 10 What Is Google Colaboratory (aka Colab)
Lecture 11 Getting Started with Colab
Lecture 12 Importing TensorFlow
Lecture 13 Sequencing Code Execution
Lecture 14 Checklist and Summary
Section 4: AI and Machine Learning Concepts
Lecture 15 The Relationship of AI to ML (Artificial Intelligence and Machine Learning)
Lecture 16 Implementing Machine Learning
Lecture 17 Creating the Model
Lecture 18 Training the Model
Lecture 19 Reducing Loss
Lecture 20 Evaluating the Trained Model
Lecture 21 What Is a Tensor
Lecture 22 Checklist and Summary
Section 5: Applying the Machine Learning Workflow with TensorFlow
Lecture 23 Setting up the First Example Project (House Price Prediction)
Lecture 24 Defining the Problem and Getting Data
Lecture 25 Exploring the Data
Lecture 26 Preparing the Data
Lecture 27 Creating the Model
Lecture 28 Training the Model
Lecture 29 Improving Performance
Lecture 30 Evaluating Model Performance
Lecture 31 Summary
Section 6: Understanding Neural Networks
Lecture 32 Machine Learning with Neural Networks
Lecture 33 How Neurons Work
Lecture 34 Neuron Architecture
Lecture 35 Activation Functions
Lecture 36 From Neurons to Neural Networks
Lecture 37 Predicting with an Untrained Neural Network
Lecture 38 Training a Neural Network
Lecture 39 Summary
Section 7: Building and Training Your First Neural Network
Lecture 40 Building a Neural Network in TensorFlow (Image Classification Project)
Lecture 41 Getting and Preparing the Data
Lecture 42 Demonstration on Creating the Model
Lecture 43 Creating the Actual Real Model
Lecture 44 Compiling the Model
Lecture 45 Training and Evaluating the Model
Lecture 46 Summary
Section 8: Monitoring and Improving Neural Network Performance
Lecture 47 Understanding the Problem with Your Model
Lecture 48 TensorBoard Setup
Lecture 49 Monitoring Your Trained Model's Performance
Lecture 50 Reducing Training Data Overfitting
Lecture 51 Randomly Dropping out Neuron Output
Lecture 52 Early Stopping
Lecture 53 Saving Your Trained Model
Lecture 54 Summary
Section 9: Deploying Your Neural Network
Lecture 55 What Is Deploying a Neural Network
Lecture 56 Installing TensorFlow ModelServer
Lecture 57 Understanding TensorFlow Model Serving
Lecture 58 Using TensorFlow Model Serving
Lecture 59 Summary
Section 10: Assignment
Lecture 60 Assignment Project
Section 11: Conclusion and Final Words
Lecture 61 What Have You Seen
Lecture 62 Training the Model
Lecture 63 Monitoring, Improving, and Deploying the Model
Lecture 64 Tips for Your Machine Learning Journey
Anyone with a curiosity for machine learning and a desire to build intelligent systems using TensorFlow.,Entrepreneurs or business professionals interested in understanding the capabilities and potential applications of artificial intelligence in their industry.,Professionals in fields such as finance, healthcare, or marketing, who want to leverage the power of neural networks for data analysis and decision-making.,Students pursuing computer science or related fields who want to explore the exciting world of machine learning.,Technology enthusiasts who want to understand the fundamentals of artificial intelligence and how neural networks work.,Software developers interested in expanding their knowledge and incorporating machine learning into their applications.,Aspiring data scientists looking to enhance their skills in deep learning and neural networks.


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