Created by M Ajmir GOOLAM HOSSEN | Last updated 11/2019

Duration: 3 hours | Video: h264, 1280x720 | Audio: AAC, 44 KHz, 2 Ch | 1.2 GB

Genre: eLearning | Language: English + Sub | 37 lectures

What you'll learn

Use Tensorflow and Keras with Python

Create Neural Network models, train them and check their accuracy

Choose the best Neural Network architecture for a given problem

Requirements

Basic programming concepts

High school Maths

Basic level software installation skills

Description

This course was designed to bring anyone up to speed on Machine Learning & Deep Learning in the shortest time.

This particular field in computer engineering has gained an exponential growth in interest worldwide following major progress in this field.

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The course starts with building on foundation concepts relating to Neural Networks. Then the course goes over Tensorflow libraries and Python language to get the students ready to build practical projects.

The course will go through four types of neural networks:

1. The simple feedforward

2. Convolutional

3. Recurrent

4. Generative Adversarial

You will build a practical Tensorflow project for each of the above Neural Networks. You will be shown exactly how to write the codes for the models, train and evaluate them.

Here is a list of projects the students will implement:

1. Build a Simple Feedforward Network for MNIST dataset, a dataset of handwritten digits

2. Build a Convolutional Network to classify Fashion items, from the Fashion MNIST dataset

3. Build a Recurrent Network to generate a text similar to Shakespeare text

4. Build a Generative Adversarial Network to generate images similar to MNIST dataset

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Who this course is for:

Those seeking entry level roles in AI/Machine Learning

Web Developers who want to implement Machine Learning for their clients

Students in Computer science

Researchers who are looking for kickstart in Deep Learnig

Software project managers who plan to use ML in clients' projects