** Deep Learning Essentials for Beginners and Experts Alike!

Deep learning is a subset of machine learning that employs neural networks with multiple layers to analyze data. These models can learn complex patterns in data without being explicitly programmed.

**Key Concepts:**

1. **Activation Functions:** Non-linear functions used to introduce complexity into the model.

2. **Loss Function:** Measures how well the model's predictions match actual values.

3. **Optimizers:** Adjust weights to minimize loss, ensuring better predictions.

4. **Autoencoders:** Type of neural network that compresses input data into a compact representation.

**Types of Autoencoders:**

1. Denoising

2. Variational

3. Sparse

**Data Compression:**

1. Lossless (e.g., Huffman Coding)

2. Lossy (e.g., JPEG, MP3)

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Learn more about deep learning essentials and how to apply them in your projects.

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Source: https://dev.to/madgan95/deep-learning-essentials-3c28