Deep Learning Project Building with Python and Keras
Deep Learning Project Building with Python and Keras, available at $54.99, has an average rating of 4.45, with 123 lectures, 4 quizzes, based on 34 reviews, and has 388 subscribers.
You will learn about Build a facial recognition project Build a happy/sad face detection project Build a simple digit recognition project using the MNIST handwritten digit database Handwritten digit recognition with advanced MNIST Build a simple linear regression model in PyCharm with TensorFlow Build a simple image recognition project using the CIFAR-10 library Image recognition with CIFAR-100 And much more! This course is ideal for individuals who are Anyone who wants to learn machine learning through practical projects with Keras, PyCharm, Python, Android Studio, Java and TensorFlow. or Anyone who wants to learn the technology that is shaping how we interact with the world. It is particularly useful for Anyone who wants to learn machine learning through practical projects with Keras, PyCharm, Python, Android Studio, Java and TensorFlow. or Anyone who wants to learn the technology that is shaping how we interact with the world.
Enroll now: Deep Learning Project Building with Python and Keras
Summary
Title: Deep Learning Project Building with Python and Keras
Price: $54.99
Average Rating: 4.45
Number of Lectures: 123
Number of Quizzes: 4
Number of Published Lectures: 123
Number of Published Quizzes: 4
Number of Curriculum Items: 127
Number of Published Curriculum Objects: 127
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Build a facial recognition project
- Build a happy/sad face detection project
- Build a simple digit recognition project using the MNIST handwritten digit database
- Handwritten digit recognition with advanced MNIST
- Build a simple linear regression model in PyCharm with TensorFlow
- Build a simple image recognition project using the CIFAR-10 library
- Image recognition with CIFAR-100
- And much more!
Who Should Attend
- Anyone who wants to learn machine learning through practical projects with Keras, PyCharm, Python, Android Studio, Java and TensorFlow.
- Anyone who wants to learn the technology that is shaping how we interact with the world.
Target Audiences
- Anyone who wants to learn machine learning through practical projects with Keras, PyCharm, Python, Android Studio, Java and TensorFlow.
- Anyone who wants to learn the technology that is shaping how we interact with the world.
You will not regret taking this course. Check out all that you’ll learn:
First we will install PyCharm 2017.2.3 and explore the interface. I will show you every step of the way. You will learn crucial Python 3.6.2 language fundamentals. Even if you have coding knowledge, going back to the basics is the keyto success as a programmer. We will build and run Python projects. I teach through practical examples, follow-alongs, and over-the-shoulder tutorials. You won’t need to go anywhere else.
Then we will install Android Studio 3 and explore the interface. You will learn how to add a simulator and build simple User Interfaces (UIs). For coding, you will learn Java 8 language fundamentals. Java is a HUGElanguage that you must know, and I will tell you all about it. We will build and run Android projects directly in the course, and you will have solid examples to apply your knowledge immediately.
With this course I will help you understand what machine learning is and compare it to Artificial Intelligence (AI). Together we will discover applications of machine learning and where we use machine learning daily. Machine learning, neural networks, deep learning, and artificial intelligence are all around us, and they’re not going away. I will show you how to get a grasp on this ever-growing technology in this course. We will explore different machine learning mechanisms and commonly used algorithms. These are popular and ones you should know.
Next I’ll teach you what TensorFlow 1.4.1 is and how it makes machine learning development easier. You will learn how to install TensorFlow and access its libraries through PyCharm. You’ll understand the basic components of TensorFlow.
Follow along with me to build a complete computational model. We’ll train and test a model and use it for future predictions. I’ll also show you how to build a linear regression model to fit a line through data. You’ll learn to train and test the model, evaluate model accuracy, and predict values using the model.
Then we’ll get started with Keras, which we’ll compare with TensorFlow to make it easier to understand, and to build your knowledge upon itself. By connecting new information with existing knowledge, you’ll form stronger connections in your brain on all of this valuable tech content. You’ll learn where and how to use Keras. By the end of this course you’ll have such a solid grasp you can add all of these technologies as qualifications on your resume, LinkedIn profile, or personal website.
We will build a basic image recognition model in PyCharm. We’ll save the trained model, export it to Android Studio, and build an app around the model.
We will follow the same process to make apps for facial recognition, facial detection, and digit recognition.
Then we will cover advanced topics and make more complexand sophisticatedprojects for recognizing handwritten digits and images from datasets.
This course was funded by a wildly successful Kickstarter
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Discover the Keras library
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Explore PyCharm and the Python language
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Explore Android Studio and the Java language
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Discover machine learning concepts
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Explore TensorFlow, a machine learning framework
What are you waiting for? Stop reading and start watching! See you there 🙂
Course Curriculum
Chapter 1: Introduction to Machine Learning + Software
Lecture 1: Update! Resources
Lecture 2: PyCharm Intro! and Topics List
Lecture 3: Android Intro! and Topics List
Lecture 4: (Files) Source Code
Chapter 2: Android Studio
Lecture 1: Downloading and Installing Android Studio
Lecture 2: Exploring Interface
Lecture 3: Setting up Emulator and Running Project
Chapter 3: Java
Lecture 1: Java Language Basics
Lecture 2: Variable Types
Lecture 3: Operations on Variables
Lecture 4: Arrays and Lists
Lecture 5: Array and List Operations
Lecture 6: If and Switch Statements
Lecture 7: While Loops
Lecture 8: For Loops
Lecture 9: Functions
Lecture 10: Parameters and Return Values
Lecture 11: Classes and Objects
Lecture 12: Superclass and Subclasses
Lecture 13: Static Variables and Axis Modifiers
Chapter 4: App Development
Lecture 1: Android App Development
Lecture 2: Building Basic User Interface
Lecture 3: Connecting UI to Backend
Lecture 4: Implementing Backend and Tidying UI
Chapter 5: Machine Learning Concepts
Lecture 1: ML Concepts Introduction
Lecture 2: How to Install PyCharm and Python
Lecture 3: Let's Explore PyCharm
Chapter 6: Python Language Basics
Lecture 1: Variables
Lecture 2: Variable Operations and Conversions
Lecture 3: Collection Types
Lecture 4: Operations on Collections
Lecture 5: Control Flow: If Statements
Lecture 6: While and For Loops
Lecture 7: Functions
Lecture 8: Classes and Objects
Lecture 9: (Files) Source Code
Chapter 7: Challenge! Python Coding Exercises
Chapter 8: TensorFlow
Lecture 1: TensorFlow Introduction
Lecture 2: Topics List
Lecture 3: How to Import TensorFlow to PyCharm
Lecture 4: Constant Nodes and Sessions
Lecture 5: Variable Nodes
Lecture 6: Placeholder Nodes
Lecture 7: Operation Nodes
Lecture 8: Loss, Optimizers, and Training
Lecture 9: Building a Linear Regression Model
Lecture 10: (Files) Source Code
Chapter 9: Image Analysis with Keras
Lecture 1: Image Analysis Introduction
Chapter 10: Simple MNIST
Lecture 1: Intro and Demo! Simple MNIST
Lecture 2: Topics List and Intro to MNIST Data
Lecture 3: Building Computational Graph
Lecture 4: Training and Testing the Model
Lecture 5: Save & Freeze Graph for Android Import
Lecture 6: Setting up Android Studio Project
Lecture 7: Building User Interface
Lecture 8: Loading Digit Images
Lecture 9: Formatting Image Data
Lecture 10: Making Prediction Using Model
Lecture 11: Displaying Results and Summary
Lecture 12: (Files) Source Code
Chapter 11: Simple CIFAR-10
Lecture 1: Intro and Demo! Simple CIFAR-10
Lecture 2: Topics List
Lecture 3: Exploring CIFAR-10 Dataset
Lecture 4: Update! CIFAR_10 Android Fix
Lecture 5: Formatting Input Data
Lecture 6: Building a Model
Lecture 7: Freezing Graph and Training Model
Lecture 8: Setting up the Android Project
Lecture 9: Setting up UI
Lecture 10: Loading and Displaying Image
Lecture 11: Formatting Image Data for Model Input
Lecture 12: Predicting and Displaying Results
Lecture 13: Summary and Outro
Lecture 14: (Files) Source Code
Chapter 12: Detecting Faces
Lecture 1: Intro and Demo! Face Detection
Lecture 2: Task List
Lecture 3: Loading Face and Non Face Images
Lecture 4: Reformatting Input Data
Lecture 5: Build Model + Write, Train & Test Scripts
Lecture 6: Freeze Graph + Train & Test Model
Lecture 7: Setting up Android Project
Lecture 8: Setting up UI
Lecture 9: Loading and Display Images
Lecture 10: Formatting Data and Running Inference
Lecture 11: Displaying Results and Summary
Instructors
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Mammoth Interactive
Top-Rated Instructor, 3.3 Million+ Students -
John Bura
Best Selling Instructor Web/App/Game Developer 1Mil Students
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 2 votes
- 3 stars: 4 votes
- 4 stars: 9 votes
- 5 stars: 17 votes
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