TensorFlow 101: Introduction to Deep Learning

TensorFlow 101: Introduction to Deep Learning, available at $34.99, has an average rating of 4.1, with 23 lectures, based on 191 reviews, and has 5180 subscribers.

You will learn about You will be able to build deep learning models for different business domains in TensorFlow You can distinguish classification and regression problems, apply supervised learning, and can develop solutions You can also apply segmentation analysis through unsupervised learning and clustering You can consume TensorFlow via Keras in easier way. Informed about tuning machine learning models to produce more successful results Learn how face recognition works This course is ideal for individuals who are One who interested in Machine Learning, Data Science and AI or Anyone who would like to learn TensorFlow framework It is particularly useful for One who interested in Machine Learning, Data Science and AI or Anyone who would like to learn TensorFlow framework.

Enroll now: TensorFlow 101: Introduction to Deep Learning

Summary

Title: TensorFlow 101: Introduction to Deep Learning

Price: $34.99

Average Rating: 4.1

Number of Lectures: 23

Number of Published Lectures: 23

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

You will be able to build deep learning models for different business domains in TensorFlow
You can distinguish classification and regression problems, apply supervised learning, and can develop solutions
You can also apply segmentation analysis through unsupervised learning and clustering
You can consume TensorFlow via Keras in easier way.
Informed about tuning machine learning models to produce more successful results
Learn how face recognition works

Who Should Attend

One who interested in Machine Learning, Data Science and AI
Anyone who would like to learn TensorFlow framework

Target Audiences

One who interested in Machine Learning, Data Science and AI
Anyone who would like to learn TensorFlow framework

This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras.

We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google FaceNet, OpenFace and Facebook DeepFace).

This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don’t have to be attend any ML course before.

Course Curriculum

Chapter 1: Perceptrons

Lecture 1: What is a Perceptron?

Lecture 2: Hands-on Perceptron

Chapter 2: Introduction

Lecture 1: Installing Tensorflow and Prerequisites on Windows

Lecture 2: Jupyter notebook

Lecture 3: Hello, TensorFlow! Building Deep Neural Networks Classifier Model

Chapter 3: Reusability in TensorFlow

Lecture 1: Restoring and Working on Already Trained Deep Neural Networks In TensorFlow

Lecture 2: Importing Saved TensorFlow DNN Classifier Model in Java

Chapter 4: Monitoring and Evaluating

Lecture 1: Monitoring Model Evaluation Metrics in TensorFlow and TensorBoard

Chapter 5: Building regression and time series models

Lecture 1: Building a DNN Regressor for Non-Linear Time Series in TensorFlow

Lecture 2: Visualizing ML Results with matplotlib and Embedding in TensorBoard

Chapter 6: Building Unsupervised Learning Models

Lecture 1: Unsupervised learning and k-means clustering with TensorFlow

Lecture 2: Applying k-means clustering to n-dimensional datasets in TensorFlow

Chapter 7: Tuning Deep Neural Network Models

Lecture 1: Optimization Algorithms in TensorFlow

Lecture 2: Activation Functions in TensorFlow

Chapter 8: Consuming TensorFlow via Keras

Lecture 1: Installing Keras

Lecture 2: Building DNN Classifier with Keras

Lecture 3: Storing and restoring a trained neural networks model with Keras

Chapter 9: Advanced applications

Lecture 1: Handwritten Digit Recognition Using Neural Networks

Lecture 2: Handwritten Digit Recognition Using Convolutional Neural Networks with Keras

Lecture 3: Transfer Learning: Consuming InceptionV3 to Classify Cat and Dog Images in Keras

Lecture 4: Tips and Tricks for Transfer Learning

Lecture 5: Autoencoders

Lecture 6: Face Recognition

Instructors

Sefik Ilkin Serengil
Software Engineer

Rating Distribution

1 stars: 13 votes
2 stars: 18 votes
3 stars: 26 votes
4 stars: 34 votes
5 stars: 100 votes

Frequently Asked Questions

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