Deep Learning with Neural Networks- Part 1

Part 1: Deep Learning Introduction

In this article, we are talking about the Introduction of Deep Learning which related to neural networks.

(1) What is Deep learning?

  • Deep learning is a subset of Machine learning in which multi-layered neural networks learn from a vast amount of data.

(2) What are the applications of the Deep learning concept?

  • Face Recognition

3. What are the tools mainly used for design & train Deep Learning Model?

  • Development Environment: Anakonda Navigator(Jupyter Notebook or Google Colab)

4. What are the main Categories of Deep Learning?

a. Supervised Deep Learning — Learned in the past to new data using labeled examples to predict future events.

b. Unsupervised Deep Learning- Used when the information used to train is neither classified nor labeled. This studies how systems can infer a function to describe a hidden structure from unlabeled data.

c . Reinforcement Deep Learning — Agent learns in an environment to achieve a long-term goal by maximizing short-term rewards.

5. Neural Networks types according to catergory of Deep Learning (Considering the Algorithm)

a. In Supervised Learning, we can mainly identify 4 categories of Neural Networks.

  • Feed-Forward Neural Network — Simple Problems

b. In Unsupervised Learning — there are mainly 2 parts

  • Autoencoder -Noise removal/filtering

c. In Reinforcement learning- there is only one that can categorize for this.

  • Network for actions, values, policies, and models -Deep Q

This article is mainly about introduction about Deep Leaning, and categories with its related neural networks. The next article is mainly focusing on Supervised learning with Feed-Forward Neural Network (FFNN)which is commonly used in deep learning applications.

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