Deep Learning Roadmap

Introduction

• Presenter: Krishna
• Platform: YouTube Channel
• Focus: Comprehensive roadmap for learning Deep Learning effectively. Understanding the path to cover essential topics helpful for interviews and applied knowledge in data science roles.
• Current Trend: Companies now require knowledge in both Deep Learning (DL) and Machine Learning (ML).

Why Deep Learning?

• Purpose: Mimic the human brain.
• Key Figure: Jeffrey Hinton and the backpropagation algorithm.

Learning Path

Foundation Concepts

• Neural Networks: Introduction, inputs, weights, bias.
• Loss Functions: Categorical cross-entropy, cross-entropy, binary cross-entropy.
• Optimizers: Gradient descent, stochastic gradient descent (SGD), AdaGrad, RMSProp, Adam Optimizer.
• Activation Functions: ReLU, Tanh, Sigmoid.
• Mathematics: Understanding the underlying math is crucial for implementation.

Key Areas in Deep Learning

1. Artificial Neural Networks (ANN)

• Concepts: Weight initialization, hyperparameter tuning, hidden layers, neurons.
• Libraries: PyTorch, Keras, TensorFlow.
• Tools: Google Colab for GPU usage.
• Deployment: Dockerize models, use Flask, deploy on cloud platforms (Heroku, AWS, Azure).
• Hyperparameter Tuning: Keras Tuner, AutoKeras.

2. Convolutional Neural Networks (CNN)

• Applications: Image and video processing.
• Understanding: Filters, strides, convolution layers, fully connected layers.
• Advanced Topics: Transfer learning, object detection (RCNN, Fast RCNN, SSD, YOLO).
• Projects: Image classification, front-end development with Flask, deploying applications.

3. Recurrent Neural Networks (RNN)

• Focus: Sequence data like sentences or sales forecasting.
• Variants: LSTM, GRU, Bidirectional LSTM.
• Pre-processing: Word embedding, word2vec.
• Advanced Models: Encoder-decoder models, attention models, Transformers (BERT).
• Libraries: Hugging Face, KTrain.

Emerging Topics

• Object Detection: RCNN, Fast RCNN, SSD, YOLO.
• Natural Language Processing (NLP): Sequence to sequence models, attention models, BERT.
• Computer Vision: Real-time applications, face recognition.

Resources

• Krishna's Playlist: 53 videos covering deep learning topics.
• Keras Blog: Detailed information on loss functions, model APIs.
• Transfer Learning: Techniques and models available on Keras Applications page.

Conclusion

• Final Advice: Practice is key. Base knowledge provided in the tutorials should be expanded through personal projects and explorations.
• Encouragement: Develop ideas, innovate on top of foundational knowledge.

Call to Action

• Subscribe: Follow the channel for more updates.

• Next Steps: Continuously learn, explore new concepts, and apply knowledge in practical settings.
• Content Updates: Upcoming videos on object detection and BERT.