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# 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.
- It is a high-level version of Machine learning which uses Artificial Neural Networks as trainable algorithms.

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

- Face Recognition
- Self Driving Cars.
- Image Classification
- Medical Diagnosis
- Ads, Search social Recommendations

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

- Development Environment: Anakonda Navigator(
**Jupyter Notebook or Google Colab**) - Modules-
**Tensorflow, Keras, ScikitLearn, OpenCV, Numpy, Matplotlib, Pandas.** - Programing Language —
**Python**

**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.

— Simple Problems*Feed-Forward Neural Network*— Pattern Recognition, Image recognition, character recognition*Convolutional Neural Networks (CNN)***Recurrent Neural Network (RNN)**— time-series data analysis, Stock market analysis, chatbot, Voice related data**Encoder-decoder architectures**-hybried, also includes Capsule and residual neural networks

**b. In Unsupervised Learning** — there are mainly 2 parts

**Autoencoder**-Noise removal/filtering**Generative Adversarial**– fake data/fake face generator, graphic design, for creating datasets

**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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