Mammoth Club All levels 12 sections 114 lectures

Deep Learning Masterclass TensorFlow JS

Deep Learning Masterclass TensorFlow JS

01
Skill level
All levels
02
Sections
12
03
Lectures
114
04
Instructor
John Bura
What's inside

This course includes.

12
Sections
114
Lectures
116
Resources
Certificate of completion
Included
Mobile and desktop access
Included
AI learning assistance
Included
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Course content

Curriculum & lectures.

9 sections · 45 lectures
+ 01a TensorFlow JS Fundamentals 4 lectures
01 What Is Machine Learning Locked
02 What Is Tensorflow Js Locked
03 Load Tensorflow Object Locked
Full course Source Files Locked
+ 01b Build Your First Tensors 4 lectures
00 Linear Algebra For Machine Learning Locked
01 Build Tensors Locked
02 Tensor Utility Methods Locked
03 Perform Math With Tensors Locked
+ 01c What is a Neural Network 3 lectures
00A What Is Deep Learning Locked
00B What Is A Neural Network Locked
Source Files Locked
+ 02 Build a Neural Network with One Hot Encoding 5 lectures
02-00 What Is One Hot Encoding Locked
02-01 Build Training Data Locked
02-02 Build The Neural Network Locked
02-03 Train The Neural Network Locked
02-04 Make A Prediction Locked
+ 03 Build a Neural Network to Detect Lines in Images 4 lectures
03-01 Build Training Data To Represent Images Locked
03-02 Build The Convolutional Neural Network Locked
03-03 Train The Convolutional Neural Network Locked
03-04 Make A Prediction Of Number Of Lines Locked
+ 04 Build an LSTM Recurrent Neural Network 5 lectures
04-00 What Is A Recurrent Neural Network Locked
04-01 Generate Sequence And Label Locked
04-02 Generate Dataset Locked
04-03 Build The Lstm Model Locked
04-04 Train The Model Locked
+ 05 Build a Model to Classify Iris Species 7 lectures
06-01 Process Iris Data Locked
06-02 Convert Data To Tensors Locked
06-03 Separate Training And Testing Data Locked
06-04 Create Training And Testing Datasets Locked
06-05 Build The Model Locked
06-06 Train The Model Locked
06-07 Make A Prediction Locked
+ 06 Build a Positive vs Negative Text Classifier 3 lectures
07-01 Load Model And Dataset Locked
07-02 Get User Input For Sentiment Analysis Locked
07-03 Make A Prediction Locked
+ 07 Build a Neural Network to Recognize Handwriting 10 lectures
08-00 What Is A Convolutional Neural Network Locked
08-01 Set Up Canvas To Load Image Data Locked
08-02 Load Mnist Dataset Locked
08-03 Separate Training And Testing Data Locked
08-04 Build The Model Locked
08-04A What Are The Network's Layers Locked
08-05 Train The Model Locked
08-06 Create Training Batches Locked
08-07 Create Testing Batches Locked
08-08 Fit Neural Network Through Data Locked
Description

About this course.

The "TensorFlow.js Neural Networks Demystified: A Beginner's Guide" is a comprehensive course designed to introduce beginners to the world of neural networks and deep learning using TensorFlow.js. This hands-on course provides a step-by-step learning experience that equips participants with the knowledge and skills to build and train neural networks in JavaScript.

The course begins by demystifying the concepts of neural networks, explaining their architecture, and how they mimic the human brain's learning process. Participants will learn about the fundamental building blocks of neural networks, such as neurons, layers, and activation functions. They will gain a solid understanding of how neural networks process and analyze data to make predictions or classify inputs.

Building on this foundation, the course dives into TensorFlow.js, a powerful JavaScript library for machine learning. Participants will learn how to set up their development environment and work with TensorFlow.js to build, train, and evaluate neural networks. They will explore various network architectures, such as feedforward networks and convolutional neural networks (CNNs), understanding their applications and implementation.

Throughout the course, participants will gain practical experience in preprocessing data, handling different types of datasets, and optimizing neural network models. They will learn techniques to enhance the performance of their models, such as regularization, dropout, and batch normalization.

The course also covers transfer learning, a technique that allows leveraging pre-trained models for specific tasks. Participants will understand how to utilize pre-trained models and adapt them to their own applications, saving time and computational resources.

Furthermore, the course explores advanced topics in neural networks, including recurrent neural networks (RNNs) for sequence data and generative adversarial networks (GANs) for generating realistic data. Participants will have the opportunity to experiment with these advanced architectures and gain a deeper understanding of their inner workings.

By the end of the "TensorFlow.js Neural Networks Demystified: A Beginner's Guide" course, participants will have a solid understanding of neural networks, deep learning concepts, and practical experience in building and training models using TensorFlow.js. They will possess the skills to create their own neural network architectures, preprocess data, and optimize models for improved performance.

This course is an ideal starting point for individuals interested in neural networks, deep learning, and JavaScript development. By leveraging the power of TensorFlow.js, participants will unlock the potential to create innovative and intelligent applications that can process and analyze complex data in real-time.

Embark on your neural network journey with the "TensorFlow.js Neural Networks Demystified: A Beginner's Guide" and gain the knowledge and skills to make impactful contributions in the exciting field of deep learning and artificial intelligence.


Instructors

Taught by people who ship.

John Bura

John Bura

Founder and CEO of Mammoth Club and Course Pro, the #1 AI-powered Learning Management System for course and content development, training and evaluation.

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Deep Learning Masterclass TensorFlow JS

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