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