About Course

Complete Tensorflow 2 and Keras Deep Learning Bootcamp – FREE

This comprehensive course offers a step-by-step guide to mastering Google’s TensorFlow 2 framework for building powerful artificial neural networks. Learn the intricacies of deep learning with this beginner-friendly approach.

Dive deep into the latest updates of TensorFlow and leverage the Keras API to effortlessly construct models. This course will equip you with the skills to create models for diverse applications such as:

* Forecasting future home prices
* Classifying medical images
* Predicting sales data
* Generating new text
* And much more!

The curriculum expertly balances theory and practical implementation with complete Jupyter Notebook guides, easy-to-follow slides, and comprehensive notes. Test your knowledge with numerous exercises designed to solidify your understanding.

**Topics covered include:**

* NumPy Crash Course
* Pandas Data Analysis Crash Course
* Data Visualization Crash Course
* Neural Network Basics
* TensorFlow Basics
* Keras Syntax Basics
* Artificial Neural Networks
* Densely Connected Networks
* Convolutional Neural Networks
* Recurrent Neural Networks
* AutoEncoders
* GANs – Generative Adversarial Networks
* Deploying TensorFlow into Production
* And much more!

Keras, a user-friendly API for machine learning, serves as the central high-level API for building and training models. Keras provides various model-building APIs (Sequential, Functional, and Subclassing) allowing you to select the best approach for your project. TensorFlow’s enhancements include eager execution for immediate iteration and intuitive debugging, and tf.data for building scalable input pipelines.

TensorFlow 2 simplifies the process of turning ideas into code and models into publication-ready results. It integrates features that enable the definition and training of cutting-edge models without compromising speed or performance.

Used by major companies worldwide such as Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and Google, TensorFlow is a powerful tool for any deep learning enthusiast.

**Embrace the world of deep learning today! This course is completely FREE and available on Udemy, Udacity, Coursera, MasterClass, NearPeer, and other platforms. Enroll now and embark on your journey to becoming a deep learning guru!**

Learn to use TensorFlow 2.0 for Deep Learning
Leverage the Keras API to quickly build models that run on Tensorflow 2
Perform Image Classification with Convolutional Neural Networks
Use Deep Learning for medical imaging
Forecast Time Series data with Recurrent Neural Networks
Use Generative Adversarial Networks (GANs) to generate images
Use deep learning for style transfer
Generate text with RNNs and Natural Language Processing
Serve Tensorflow Models through an API
Use GPUs for accelerated deep learning

Course Content

Course Overview, Installs, and Setup

A Message from the Professor
Course Overview

04:38
Course Setup and Installation

21:49
Course Material Download Link

00:00

NumPy Crash Course

Pandas Crash Course

Introduction to Pandas

03:57
Pandas Series

08:40
Pandas DataFrames – Part One

11:14
Pandas DataFrames – Part Two

09:32
Pandas Missing Data

10:07
GroupBy Operations

09:40
Pandas Operations

13:51
Data Input and Output

11:51
Pandas Exercises

02:41
Pandas Exercises – Solutions

07:17

Visualization Crash Course

Machine Learning Concepts Overview

Basic Artificial Neural Networks – ANNs

Introduction to ANN Section

02:15
Perceptron Model

10:39
Neural Networks

07:19
Activation Functions

10:39
Multi-Class Classification Considerations

10:34
Cost Functions and Gradient Descent

18:13
Backpropagation

14:47
TensorFlow vs. Keras Explained

02:13
Keras Syntax Basics – Part One – Preparing the Data

10:48
Keras Syntax Basics – Part Two – Creating and Training the Model

13:59
Keras Syntax Basics – Part Three – Model Evaluation

12:56
Keras Regression Code Along – Exploratory Data Analysis

18:50
Keras Regression Code Along – Exploratory Data Analysis – Continued

13:15
Keras Regression Code Along – Data Preprocessing and Creating a Model

08:42
Keras Regression Code Along – Model Evaluation and Predictions

11:23
Keras Classification Code Along – EDA and Preprocessing

08:05
Keras Classification – Dealing with Overfitting and Evaluation

16:50
TensorFlow 2.0 Keras Project Options Overview

01:40
TensorFlow 2.0 Keras Project Notebook Overview

07:41
Keras Project Solutions – Exploratory Data Analysis

20:35
Keras Project Solutions – Dealing with Missing Data

14:46
Keras Project Solutions – Dealing with Missing Data – Part Two

12:02
Keras Project Solutions – Categorical Data

17:23
Keras Project Solutions – Data PreProcessing

03:45
Keras Project Solutions – Creating and Training a Model

03:57
Keras Project Solutions – Model Evaluation

09:42
Tensorboard

18:22

Convolutional Neural Networks – CNNs

CNN Section Overview

01:33
Image Filters and Kernels

11:35
Convolutional Layers

14:01
Pooling Layers

06:47
MNIST Data Set Overview

04:41
CNN on MNIST – Part One – The Data

12:57
CNN on MNIST – Part Two – Creating and Training the Model

16:14
CNN on MNIST – Part Three – Model Evaluation

06:53
CNN on CIFAR-10 – Part One – The Data

11:23
CNN on CIFAR-10 – Part Two – Evaluating the Model

07:05
Downloading Data Set for Real Image Lectures

05:22
CNN on Real Image Files – Part One – Reading in the Data

14:54
CNN on Real Image Files – Part Two – Data Processing

15:37
CNN on Real Image Files – Part Three – Creating the Model

13:36
CNN on Real Image Files – Part Four – Evaluating the Model

08:49
CNN Exercise Overview

02:10
CNN Exercise Solutions

08:31

Recurrent Neural Networks – RNNs

RNN Section Overview

02:38
RNN Basic Theory

07:41
Vanishing Gradients

06:47
LSTMS and GRU

11:23
RNN Batches

07:49
RNN on a Sine Wave – The Data

08:29
RNN on a Sine Wave – Batch Generator

08:15
RNN on a Sine Wave – Creating the Model

15:20
RNN on a Sine Wave – LSTMs and Forecasting

13:24
RNN on a Time Series – Part One

09:49
RNN on a Time Series – Part Two

21:37
RNN Exercise

04:09
RNN Exercise – Solutions

21:49
Bonus – Multivariate Time Series – RNN and LSTMs

16:12

Natural Language Processing

AutoEncoders

Generative Adversarial Networks

Deployment

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

Student Ratings & Reviews

5.0

Total 2 Ratings

2 Ratings

0 Rating

Zain Ali Shah

4 months ago

This course is very informative

Abdul Rehman Memon

5 months ago

amazing

About Course

Complete Tensorflow 2 and Keras Deep Learning Bootcamp – FREE

What Will You Learn?

Course Content

Course Overview, Installs, and Setup

A Message from the Professor

Course Overview

Course Setup and Installation

Course Material Download Link

NumPy Crash Course

Introduction to NumPy

NumPy Arrays

Numpy Index Selection

NumPy Operations

NumPy Exercises

Numpy Exercises – Solutions

Pandas Crash Course

Introduction to Pandas

Pandas Series

Pandas DataFrames – Part One

Pandas DataFrames – Part Two

Pandas Missing Data

GroupBy Operations

Pandas Operations

Data Input and Output

Pandas Exercises

Pandas Exercises – Solutions

Visualization Crash Course

Introduction to Python Visualization

Matplotlib Basics

Seaborn Basics

Data Visualization Exercises

Data Visualization Exercises – Solutions

Machine Learning Concepts Overview

What is Machine Learning

Supervised Learning Overview

Overfitting

Evaluating Performance – Classification Error Metrics

Evaluating Performance – Regression Error Metrics

Unsupervised Learning

Basic Artificial Neural Networks – ANNs

Introduction to ANN Section

Perceptron Model

Neural Networks

Activation Functions

Multi-Class Classification Considerations

Cost Functions and Gradient Descent

Backpropagation

TensorFlow vs. Keras Explained

Keras Syntax Basics – Part One – Preparing the Data

Keras Syntax Basics – Part Two – Creating and Training the Model

Keras Syntax Basics – Part Three – Model Evaluation

Keras Regression Code Along – Exploratory Data Analysis

Keras Regression Code Along – Exploratory Data Analysis – Continued

Keras Regression Code Along – Data Preprocessing and Creating a Model

Keras Regression Code Along – Model Evaluation and Predictions

Keras Classification Code Along – EDA and Preprocessing

Keras Classification – Dealing with Overfitting and Evaluation

TensorFlow 2.0 Keras Project Options Overview

TensorFlow 2.0 Keras Project Notebook Overview

Keras Project Solutions – Exploratory Data Analysis

Keras Project Solutions – Dealing with Missing Data

Keras Project Solutions – Dealing with Missing Data – Part Two

Keras Project Solutions – Categorical Data

Keras Project Solutions – Data PreProcessing

Keras Project Solutions – Creating and Training a Model

Keras Project Solutions – Model Evaluation

Tensorboard

Convolutional Neural Networks – CNNs

CNN Section Overview

Image Filters and Kernels

Convolutional Layers

Pooling Layers

MNIST Data Set Overview

CNN on MNIST – Part One – The Data

CNN on MNIST – Part Two – Creating and Training the Model

CNN on MNIST – Part Three – Model Evaluation

CNN on CIFAR-10 – Part One – The Data

CNN on CIFAR-10 – Part Two – Evaluating the Model

Downloading Data Set for Real Image Lectures