Created by M Ajmir GOOLAM HOSSEN | Last updated 11/2019Duration: 3 hours | Video: h264, 1280×720 | Audio: AAC, 44 KHz, 2 Ch | 1.2 GBGenre: eLearning | Language: English + Sub | 37 lecturesWhat you’ll learnUse Tensorflow and Keras with PythonCreate Neural Network models, train them and check their accuracyChoose the best Neural Network architecture for a given problemRequirementsBasic programming conceptsHigh school MathsBasic level software installation skillsDescriptionThis 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.

\nThe 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 feedforward2. Convolutional3. Recurrent4. Generative AdversarialYou 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 digits2. Build a Convolutional Network to classify Fashion items, from the Fashion MNIST dataset3. Build a Recurrent Network to generate a text similar to Shakespeare text4. Build a Generative Adversarial Network to generate images similar to MNIST dataset\nWho this course is for:Those seeking entry level roles in AI/Machine LearningWeb Developers who want to implement Machine Learning for their clientsStudents in Computer scienceResearchers who are looking for kickstart in Deep LearnigSoftware project managers who plan to use ML in clients' projects