Learn Deep Learning (Neural Networks) Models from scratch

By ridhigrg |Email | Nov 19, 2019 | 6687 Views

Neural Networks and Deep Learning
Offered By deeplearning.ai
About this Course
If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. 

In this course, you will learn the foundations of deep learning. When you finish this class, you will:
  • Understand the major technology trends driving Deep Learning
  • Be able to build, train and apply fully connected deep neural networks 
  • Know how to implement efficient (vectorized) neural networks 
  • Understand the key parameters in a neural network's architecture 

This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. 

This is the first course of the Deep Learning Specialization.

Convolutional Neural Networks in TensorFlow
Offered By deeplearning.ai
About this Course
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built-in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer "sees" information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. 

The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Introduction to Deep Learning & Neural Networks with Keras
Offered By IBM
About this Course
Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.

After completing this course, learners will be able to:
  • describe what a neural network is, what a deep learning model is, and the difference between them.
  • demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.
  • demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.
  • build deep learning models and networks using the Keras library.

Convolutional Neural Networks
Offered By deeplearning.ai
About this Course
This course will teach you how to build convolutional neural networks and apply them to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving to accurate face recognition, to automatic reading of radiology images. 

You will:
  • Understand how to build a convolutional neural network, including recent variations such as residual networks.
  • Know how to apply convolutional networks to visual detection and recognition tasks.
  • Know to use neural style transfer to generate art.
  • Be able to apply these algorithms to a variety of images, videos, and other 2D or 3D data.

This is the fourth course of the Deep Learning Specialization.

Source: HOB