Deep Learning with Tensorflow 2.0

What you’ll learn

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

We have added plenty of supplemental resources to our lectures (Excel sheets, course notes) in an effort to enhance your learning experience throughout the course. You can click on link below to go to Download Page for Resources :

Note : Download Link Only Available for Enrolled Students

Module Content

Introduction 3 Lessons

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Meet your instructors and why you should study machine learning

What does this course cover?

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Introduction to neural networks 12 Lessons

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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

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Setting up the environment 8 Lessons

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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

Installing packages – exercise

Installing packages – solution

Minimal Example – your first machine learning algorithm 4 Lessons

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Minimal example – part 1

Minimal example – part 2

Minimal example – part 3

Minimal example – part 4

TensorFlow – An Introduction 7 Lessons

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TensorFlow outline

TensorFlow 2 intro

A Note on Coding in TensorFlow

Types of file formats in TensorFlow and data handling

Model layout – inputs, outputs, targets, weights, biases, optimizer and loss

Interpreting the result and extracting the weights and bias

Cutomizing your model

Going deeper : Introduction to deep neural networks 8 Lessons

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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 6 Lessons

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Underfitting and overfitting

Underfitting and overfitting – classification

Training and validation

Training, validation, and test

N-fold cross validation

Early stopping

Initialization 3 Lessons

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Initialization – Introduction

Types of simple initializations

Xavier initialization

Gradient descent and learning rates 7 Lessons

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Stochastic gradient descent

Gradient descent pitfalls

Momentum

Learning rate schedules

Learning rate schedules. A picture

Adaptive learning rate schedules

Adaptive moment estimation

Preprocessing 5 Lessons

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Preprocessing introduction

Basic preprocessing

Standardization

Dealing with categorical data

One-hot and binary encoding

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Module Includes

13 Sections
91 Lessons
Module Certificate