Deep Learning - Deep Neural Network for Beginners Using Python

Description

In this course, you will quickly learn how to build DNNs (Deep Neural Networks) and how to train them. This learning-by-doing course will also help you master the elementary concepts and methodology with Python. You need to have a basic knowledge of python to get the best out of this course.

Are you ready to start your path to becoming a deep learning expert? Then this course is for you. This course is step-by-step. In every new tutorial, we build on what we have already learned and move one extra step forward, and then we assign you a small task that is solved at the beginning of the next video. We start by teaching the theoretical part of the concept, and then implement everything as it is practically using Python. This comprehensive course will be your guide to learning how to use the power of Python to train your machine such that your machine starts learning just like humans, and based on that learning, your machine starts making predictions as well! We will be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine learning. Python will be taught from the elementary level up to an advanced level so that any machine learning concept can be implemented. You will also learn various steps of data preprocessing, which allows us to make data ready for machine learning algorithms. You will learn the general concepts of machine learning overall, which will be followed by the implementation of one of the essential ML algorithms, 'Deep Neural Networks'. Each concept of DNNs will be taught theoretically and will be implemented using Python. By the end of this course, you will be able to understand the methodology of DNNs with deep learning using real-world datasets. All the resources for this course are available at: https://github.com/PacktPublishing/Deep-Learning---Deep-Neural-Network-for-Beginners-Using-Python

Learn the basics of machine learning and neural networks
Understand the architecture of neural networks
Learn the basics of training a DNN using the Gradient Descent algorithm
Learn how to implement a complete DNN using NumPy
Learn to create a complete structure for DNN from scratch using Python
Work on a project using deep learning for the IRIS dataset

This course is designed for anyone who is interested in data science or interested in taking their data-speak to a higher level.

Students who want to master DNNs with real datasets in deep learning or who want to implement DNNs in realistic projects can also benefit from the course. You need to have a background in deep learning to get the best out of this course.

This course is clear and easy to understand as it is designed for absolute beginners. In this course, we will start by first learning the theoretical part of a concept followed by implementing it practically, using Python.

Covers the most sophisticated and recently discovered DNN models by eminent data scientists * Relate the theoretical concepts and sequence modeling with DNNs * Understand the methodology of DNNs with deep learning using real-world datasets

https://github.com/PacktPublishing/Deep-Learning---Deep-Neural-Network-for-Beginners-Using-Python

AI Sciences are a group of experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

1. Section 1: Introduction

2. Section 2: Basics of Deep Learning

3. Section 3: Deep Learning

4. Section 4: Optimizations

5. Section 5: Final Project