Deep Learning Neural Network Tutorials

Deep Learning Neural Network Tutorials

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How to Train Deep Learning Model on Google Colab for FREE | Train Neural Network on GPU Machine

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1 of 30

How to Train Deep Learning Model on Google Colab for FREE | Train Neural Network on GPU Machine

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Deep Learning Neural Network Tutorials

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  1. 1 How to Train Deep Learning Model on Google Colab for FREE | Train Neural Network on GPU Machine
  2. 2 Google Colaboratory for free GPU -(Neural Network Model Training) | Part 2
  3. 3 Differentiate between Deep Learning and Machine Learning | Tensorflow Tutorial Series
  4. 4 Tensorflow Tutorial Series Introduction | A Hands-on Learning Experience
  5. 5 What is Deep Learning | Tensorflow Tutorial Series
  6. 6 Artificial Neural Network Tutorial | Tensorflow Tutorial Series
  7. 7 Activation Functions in Neural Networks | Tensorflow Tutorial Series
  8. 8 How Neural Network gets Trained | Tensorflow firsthand Tutorial Series for Beginners
  9. 9 Google Colaboratory for Tensorflow | Tensorflow firsthand Tutorial Series for Beginners
  10. 10 Tensorflow Math Operations using Constants | Tensorflow Tutorial Series
  11. 11 How Data travels in Deep Neural Networks | Scalar vs Vector vs Matrix vs Tensor
  12. 12 What are Placeholders in Tensorflow | Usage of Placeholders in Tensorflow
  13. 13 Tensorflow Variables and Associated Computations | Optimize Model parameter during Training
  14. 14 What is Loss Function in Deep Learning | Loss Function in Machine Learning | Loss Function Types
  15. 15 Backpropagation Explained in a simple manner | Backpropagation in Neural Networks
  16. 16 Learning from the past events using Recurrent Neural Network | A Gentle introduction to RNN
  17. 17 Basic Building Blocks of Recurrent Neural Network | Recurrent Neural Network (RNN/LSTM)
  18. 18 Cases where Backpropagation fails in Neural Networks | Inherent problems with Recurrent Neural Net
  19. 19 Why Long Memory Neurons are Important in Recurrent Neural Network | Deep Learning
  20. 20 Understand LSTM cells to build Neural Network based Applications | LSTM Architecture
  21. 21 Convert Text into Numeric Encoding for Recurrent Neural Network | How RNN read Text Data
  22. 22 Convolution Neural Network (CNN) Introduction and Intuition | Convolution Neural Network Explained
  23. 23 How to Detect Features of an Image using CNN (Convolution Neural Network)?
  24. 24 Why Rectified Linear Unit (ReLU) is required in CNN? | ReLU Layer in CNN
  25. 25 Why do we use max POOLING Layer in CNN | What is Pooling Layer in CNN?
  26. 26 Why do we use Flattening Layer in CNN | What is Flattening Layer in CNN?
  27. 27 How to address Overfitting in Neural Network using Dropout Layer | What is Dropout Layer in CNN?
  28. 28 What is Fully Connected Layer | How does Fully Connected Layer works
  29. 29 How to Utilize Pre-Trained Models for building Deep Learning Models | VGG16 ResNET Object Detection
  30. 30 Increase ACCURACY of Model on Small Dataset | DATA AUGMENTATION for Small Image Dataset

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