Deep Learning | What is Deep Learning? | Deep Learning Tutorial For Beginners | 2023 | Simplilearn

Step-by-Step Tutorial: Understanding Deep Learning

1. Introduction to Deep Learning

   • Deep learning is a subset of machine learning and artificial intelligence.
   • It involves training algorithms with data to mimic human behavior.

2. Comparison with Machine Learning

   • Machine learning uses algorithms trained with data to achieve AI.
   • Deep learning is inspired by the structure of the human brain.

3. Differentiating Between Machine Learning and Deep Learning

   • In machine learning, features to differentiate objects are based on human-defined criteria like size and shape.
   • In deep learning, neural networks autonomously pick out features without human intervention, requiring a large volume of data for training.

4. Neural Networks and Handwritten Digits

   • Illustration with three students writing the digit 9 on paper.
   • Each digit is represented as an image of 28x28 pixels, totaling 784 pixels.
   • Neurons in a neural network process information, with each pixel fed into a neuron in the input layer.

5. Working of Neural Networks

   • Information is transferred between layers through weighted channels.
   • Neurons have biases added to the weighted sum of inputs, followed by activation functions to determine neuron activation.
   • Activated neurons pass information to subsequent layers until the output layer identifies the input digit.

6. Applications of Deep Learning

   • Deep learning is applied in various fields:
     • Customer support for chatbots.
     • Medical care for cancer cell detection and MRI analysis.
     • Self-driving cars by companies like Apple, Tesla, and Nissan.

7. Limitations of Deep Learning

   • Requires a massive volume of data for training.
   • Demands high computational power, often using graphical processing units (GPUs).
   • Training time can range from hours to months, increasing with data volume and network complexity.

8. Popular Deep Learning Frameworks

   • TensorFlow, PyTorch, Keras, Deeplearning4j, Café, and Microsoft Cognitive Toolkit are some popular frameworks.

9. Future Predictions

   • Companies like Horus Technology are developing devices for the blind using deep learning and computer vision.
   • The future of deep learning and AI holds many surprises and advancements.

10. Conclusion

    • Deep learning has a vast scope and potential applications in various industries.
    • The future of deep learning and AI is promising, with ongoing advancements and innovations.

By following these steps, you can gain a better understanding of deep learning, its applications, and its significance in the field of artificial intelligence.