Practical Deep Learning with Tensorflow 2.x and Keras

Practical Deep Learning with Tensorflow 2.x and Keras, available at $59.99, has an average rating of 4.6, with 34 lectures, based on 437 reviews, and has 2593 subscribers.

You will learn about Be able to run deep learning models with Keras on Tensorflow 2 backend Run Deep Neural Networks on a real-world scientific protein dataset Understand how to feed own data to deep learning models (i.e. handling the notorious shape mismatch issue) Understand Deep Learning, CNN, dropout, functional API with minimal of math Understand and use Keras functional API to create models with multiple inputs and outputs Learn how to do Transfer Learning practically Stunning SUPPORT. I answer questions on the same day. This course is ideal for individuals who are Anyone who wants to learn machine learning (this course is a soft introduction) or Anyone who knows machine learning and wants to learn deep learning (this course focuses on deep learning) or Anyone who knows deep learning but needs help applying their knowledge in practice (this is a very applied course) or Anyone who is comfortable with deep learning models but has trouble processing examples beyond the toy examples covered in typical courses (this course has a real-world case study and not just toy examples) or Anyone who is a researcher or educator?working in machine learning and wants to move from theory to practice It is particularly useful for Anyone who wants to learn machine learning (this course is a soft introduction) or Anyone who knows machine learning and wants to learn deep learning (this course focuses on deep learning) or Anyone who knows deep learning but needs help applying their knowledge in practice (this is a very applied course) or Anyone who is comfortable with deep learning models but has trouble processing examples beyond the toy examples covered in typical courses (this course has a real-world case study and not just toy examples) or Anyone who is a researcher or educator?working in machine learning and wants to move from theory to practice.

Enroll now: Practical Deep Learning with Tensorflow 2.x and Keras

Summary

Title: Practical Deep Learning with Tensorflow 2.x and Keras

Price: $59.99

Average Rating: 4.6

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $79.99

Quality Status: approved

Status: Live

What You Will Learn

Be able to run deep learning models with Keras on Tensorflow 2 backend

Run Deep Neural Networks on a real-world scientific protein dataset

Understand how to feed own data to deep learning models (i.e. handling the notorious shape mismatch issue)

Understand Deep Learning, CNN, dropout, functional API with minimal of math

Understand and use Keras functional API to create models with multiple inputs and outputs

Learn how to do Transfer Learning practically

Stunning SUPPORT. I answer questions on the same day.

Who Should Attend

Anyone who wants to learn machine learning (this course is a soft introduction)

Anyone who knows machine learning and wants to learn deep learning (this course focuses on deep learning)

Anyone who knows deep learning but needs help applying their knowledge in practice (this is a very applied course)

Anyone who is comfortable with deep learning models but has trouble processing examples beyond the toy examples covered in typical courses (this course has a real-world case study and not just toy examples)

Anyone who is a researcher or educator?working in machine learning and wants to move from theory to practice

Target Audiences

Anyone who wants to learn machine learning (this course is a soft introduction)

Anyone who knows machine learning and wants to learn deep learning (this course focuses on deep learning)

Anyone who knows deep learning but needs help applying their knowledge in practice (this is a very applied course)

Anyone who is comfortable with deep learning models but has trouble processing examples beyond the toy examples covered in typical courses (this course has a real-world case study and not just toy examples)

Anyone who is a researcher or educator?working in machine learning and wants to move from theory to practice

**UPDATED: Now using Tensorflow 2. Please post in Q&A if you have any trouble. I’m here to help**

**UPDATED 11-2021: Added a section on Practical Transfer Learning**

TensorFlow is by far, the most popular library for deep learning. Backed by Google, it is a solid investment of your time and efforts if you want to succeed in the area of machine learning and AI. The issue most people face is that getting started with Tensorflow guides usually delve too deeply into unnecessary mathematics.

That is where this course comes in. While some theory is important, a lot of it is just not needed when you’re just getting started!

This course is for you if you are new to Machine Learning but want to learn it without all the complicated math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems.

In this course, we will start from very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code. You will be using Keras and Tensorflow 2.x one of the easiest and most powerful machine learning tools out there.

You will start with a basic model of how machines learn and then move on to higher models such as:

Convolutional Neural Networks 

Residual Connections 

Inception Module

Functional API of Keras / Tensorflow 2.x

Transfer Learning

In this course, we explain concepts using not only toy datasets but also a real-world dataset from the bioinformatics domain. While you may not be interested in this particular domain, you would still learn a lot of important concepts that are involved in taking data from the real world and feeding it to ML models. This is the aspect of ML that is missing from almost all courses available on the internet today! Doing this would mean that you would be able to solve problems of your own industry after finishing this course.

All with only a few lines of code. All the examples used in the course come with a starter code that will get you started and remove the grunt effort. The course also includes finished codes for the examples run in the videos so that you can see the end product should you ever get stuck.  Do checkout the preview lectures on this page to get a better feel of the teaching style used in this course and how it can help you learn quickly.

I provide unmatched support. All questions are answered within 24 hours. Try me and see   =]

Course Curriculum

Chapter 1: Introduction

Lecture 1: About the Instructor

Lecture 2: Dive into Machine Learning

Lecture 3: Making Predictions

Chapter 2: A Bit of Theory

Lecture 1: Machine Learning Pipeline

Lecture 2: Regression

Lecture 3: Binary and Multi-class Classification

Lecture 4: Recap and a Link to More Theory

Chapter 3: Installation and Setup

Lecture 1: Environment setup for Windows (and some issues with it)

Lecture 2: Environment setup for Mac and Linux

Chapter 4: Say Hi to Keras

Lecture 1: Data Preparation

Lecture 2: Training and Testing

Lecture 3: Using TensorBoard to Visualize Learning

Lecture 4: Google Colaboratory for Free GPU/TPU Access, Saving to Google Drive

Chapter 5: Real World Case Study: Predicting Protein Functions

Lecture 1: Problem Description and Data View

Lecture 2: Pre-processing the Data

Lecture 3: Loading Data and Getting the Shapes Right

Lecture 4: Train, Test Split

Lecture 5: Shapes in Depth (or how not to have headaches for days)

Lecture 6: Sequential Model

Lecture 7: Functional API

Chapter 6: Convolutional Neural Networks (CNN)

Lecture 1: Basics and Rationale

Lecture 2: CNN in Keras (or why Keras is better than your ML tool)

Lecture 3: Pooling (and why its not that important)

Lecture 4: Dropout (and why you should always consider it)

Chapter 7: Graph-based Models

Lecture 1: Functional API for CNN

Lecture 2: Inception Module

Lecture 3: Residual Connections

Chapter 8: Finishing Touches

Lecture 1: Saving and Loading Model Weights

Lecture 2: Parting Words

Chapter 9: Transfer Learning Practical

Lecture 1: Basic Theory Behind Transfer Learning

Lecture 2: Re-using Models (without Transfer Learning)

Lecture 3: Introducing the Transfer in the Learning + Fine-Tuning

Chapter 10: Extra Resources

Lecture 1: Machine Learning Yearning Book (Free Download)

Lecture 2: Bonus Lecture

Instructors

Dr. Mohammad Nauman
Helping YOU learn software engineering, quickly and easily!

Rating Distribution

1 stars: 5 votes

2 stars: 11 votes

3 stars: 51 votes

4 stars: 154 votes

5 stars: 216 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!