Introduction to Deep Learning and Neural Networks Course

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In this repository, you will find the solutions of all coding challenges and jupyter notebooks

This course is an accumulation of well-grounded knowledge and experience in deep learning. It provides you with the basic concepts you need in order to start working with and training various machine learning models. You will cover both basic and intermediate concepts including but not limited to: convolutional neural networks, recurrent neural networks, generative adversarial networks as well as transformers. After completing this course, you will have a comprehensive understanding of the fundamental architectural components of deep learning. Whether you’re a data and computer scientist, computer and big data engineer, solution architect, or software engineer, you will benefit from this course.

Takeaway Skills

• Understanding of the most popular Deep Learning models
• A solid grasp on the mathematics and the intuition behind the algorithms
• A good experience with Deep Learning Programming and Pytorch

Course Contents

1. Learn Deep Learning

   1. About this Course
   2. Why Learn Deep Learning?
   3. Overview of the Course
   4. Neural Networks

2. Neural Networks

   1. Linear Classifiers
   2. Optimization and Gradient Descent
   3. Neural Networks
   4. Backpropagation Algorithm
   5. Build a Neural Network With Pytorch
   6. Quiz Yourself on Neural Networks
   7. Training Neural Networks

3. Training Neural Networks

   1. Optimization
   2. Popular Optimization Algorithms
   3. Activation Functions
   4. Training in Pytorch
   5. Quiz Yourself on Training Neural Networks
   6. Convolutional Neural Networks

4. Convolutional Neural Networks

   1. The Principles of the Convolution
   2. Convolution in Practice
   3. Build a Convolutional Network
   4. Batch Normalization and Dropout
   5. Skip Connections
   6. CNN Architectures
   7. Quiz Yourself on CNNs

5. Recurrent Neural Networks

   1. A Simple RNN Cell
   2. LSTM: Long Short Term Memory Cells
   3. Writing a Custom LSTM Cell in Pytorch
   4. Connect LSTM Cells Across Time and Space
   5. Quiz Yourself on RNNs

6. Autoencoders

   1. Generative Learning
   2. Basics of Autoencoders
   3. Variational Autoencoder: Theory
   4. Variational Autoencoder: Practice
   5. Quiz Yourself on Autoencoders

7. Generative Adversarial Networks

   1. Generator and Discriminator
   2. Generative Adversarial Networks in Detail
   3. Develop a GAN with Pytorch
   4. Quiz Yourself on GANs
   5. Attention and Transformers

8. Attention and Transformers

   1. Sequence to Sequence Models
   2. Attention
   3. Key Concepts of Transformers
   4. Self-Attention
   5. Multi-Head Self-Attention
   6. Transformers Building Blocks
   7. The Transformer's Encoder
   8. The Transformer's Decoder
   9. Build a Transformer Encoder
   10. Quiz Yourself on Transformers
   11. Graph Neural Networks

9. Graph Neural Networks

   1. Basics of Graphs
   2. Mathematics for Graphs
   3. Graph Convolutional Networks
   4. Implementation of a GCN
   5. Quiz Yourself on GNNs

10. Conclusion

    1. Where to go from here?
    2. Appendix
    3. FInal Quiz