Deep Learning with TensorFlow 2.0

About Course

Learn Deep Learning with TensorFlow 2.0 for free! This comprehensive course covers everything from the basics of deep neural networks to advanced optimization techniques. You’ll get hands-on experience with Google’s TensorFlow, build your own deep learning algorithm, and learn how to apply deep learning to real-world business problems.

This free course is offered through Theetay, a platform that provides access to the best online courses from Udemy, Udacity, Coursera, MasterClass, NearPeer, and more.

Here’s what you’ll learn in this Deep Learning course:

Master the fundamentals of deep learning and deep neural networks
Gain practical experience with TensorFlow and NumPy
Explore various layers, their building blocks, and activations (sigmoid, tanh, ReLu, softmax, etc.)
Understand the backpropagation process
Learn how to prevent overfitting
Discover state-of-the-art initialization methods
Build deep neural networks using real-world data with provided templates
Create your own deep learning algorithm in just one hour

This course is designed for beginners with a basic understanding of Python programming. Our engaging videos and step-by-step approach make it easy to learn, even if you’re new to deep learning.

Enroll in this free Deep Learning course today and start your journey to becoming a master of this in-demand field.

Gain a Strong Understanding of TensorFlow - Google’s Cutting-Edge Deep Learning Framework
Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow
Set Yourself Apart with Hands-on Deep and Machine Learning Experience
Grasp the Mathematics Behind Deep Learning Algorithms
Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules
Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization
Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding

Course Content

Welcome! Course Introduction

A Message from the Professor
Meet your instructors and why you should study machine learning

06:54
What does the course cover

04:14

Introduction to Neural networks

Introduction to neural networks

04:09
Training the model

02:55
Types of machine learning

03:43
The linear model

03:09
The linear model. Multiple inputs

02:25
The linear model. Multiple inputs and multiple outputs

04:25
Graphical representation

01:47
The objective function

01:27
L2-norm loss

02:04
Cross-entropy loss

03:56
One parameter gradient descent

06:33
N-parameter gradient descent

06:08

Setting up the working environment

Minimal Example – Your first machine learning algorithm

Tensorflow – An introduction

Going deeper – Introduction to Deep Neural Networks

Overfitting

Initialization

Gradient descent and learning rates

Preprocessing

The MNIST example

The dataset

02:25
How to tackle the MNIST

02:44
Importing the relevant packages and load the data

02:11
Preprocess the data – create a validation dataset and scale the data

04:43
Preprocess the data – shuffle and batch the data

06:30
Outline the model

04:54
Select the loss and the optimizer

02:05
Learning

05:38
Testing the model

03:56

Business case

Appendix: Linear Algebra Fundamentals

What is a Matrix

03:37
Scalars and Vectors

02:58
Linear Algebra and Geometry

03:06
Scalars Vectors and Matrices in Python

05:09
Tensors

03:00
Addition and Subtraction of Matrices

03:36
Errors when Adding Matrices

02:01
Transpose of a Matrix

05:13
Dot Product of Vectors

03:48
Dot Product of Matrices

08:23
Why is Linear Algebra Useful

10:10

Conclusion

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Student Ratings & Reviews

5.0

Total 1 Rating

1 Rating

0 Rating

PM Liew

6 months ago

A very good starting course do Deep Neural Network and TF2

About Course

What Will You Learn?

Course Content

Welcome! Course Introduction

A Message from the Professor

Meet your instructors and why you should study machine learning

What does the course cover

Introduction to Neural networks

Introduction to neural networks

Training the model

Types of machine learning

The linear model

The linear model. Multiple inputs

The linear model. Multiple inputs and multiple outputs

Graphical representation

The objective function

L2-norm loss

Cross-entropy loss

One parameter gradient descent

N-parameter gradient descent

Setting up the working environment

Setting up the environment – An introduction – Do not skip please!

Why Python and why Jupyter

Installing Anaconda

The Jupyter dashboard – part 1

The Jupyter dashboard – part 2

Installing TensorFlow 2

Minimal Example – Your first machine learning algorithm

Minimal example – part 1

Minimal example – part 2

Minimal example – part 3

Minimal example – part 4

Tensorflow – An introduction

TensorFlow outline

TensorFlow 2 intro

A Note on Coding in TensorFlow

Types of file formats in TensorFlow and data handling

Model layout – inputs outputs tar

Interpreting the result and extracting the weights and bias

Cutomizing your model

Going deeper – Introduction to Deep Neural Networks

Layers

What is a deep net

Understanding deep nets in depth

Why do we need non-linearities

Activation functions

Softmax activation

Backpropagation

Backpropagation – visual representation

Overfitting

Underfitting and overfitting

Underfitting and overfitting – classification

Training and validation

Training validation and test

N-fold cross validation

Early stopping

Initialization

Initialization – Introduction

Types of simple initializations

Xavier initialization

Gradient descent and learning rates

Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing

Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

The MNIST example

The dataset

How to tackle the MNIST

Importing the relevant packages and load the data

Preprocess the data – create a validation dataset and scale the data

Preprocess the data – shuffle and batch the data