- An Introduction to Deep Learning
- Understanding Fundamentals of Neural Networks using TensorFlow
- Explanation of the Neural Networks using TensorFlow
- Master Deep Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Restricted Boltzmann Machine (RBM) and Autoencoders
- An Introduction to Keras
- An Introduction to TFlearn)
1. An Introduction to Deep Learning
- An overview of Deep Learning
- Deep Learning- A massive change in the Artificial Intelligence
- An overview of Machine Learning
- Limitations of Machine Learning
- Reasons to go with Deep Learning over Machine Learning
2. Understanding Fundamentals of Neural Networks using TensorFlow
- Work process of Deep Learning
- Different Activation Functions
- How Deep Learning Works?
- Activation Functions
- A Brief of Perceptron
- Training a Perceptron
- Key Parameters of Perceptron
- An explanation of Tensorflow?
- TensorFlow and its code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Creating a Model
- Step-by-Step process of Use-Case Implementation
3. Explanation of the Neural Networks using TensorFlow
- An overview of the limitations of a single Perceptron
- Knowing the limitations of A Single Perceptron
- Know Neural Networks in-depth
- Explanation of Multi-layer Perceptron
- Backpropagation- Learning Algorithm
- An overview of Backpropagation- Using Neural Network with Examples
- MLP Digit-Classifier using TensorFlow
- TensorBoard
- Summary
4. Master Deep Networks
- Why go to the Deep Learning?
- Classification of the SONAR Dataset
- What is Deep Learning?
- Extraction of the Features
- Work process of the Deep Network
- Training using Backpropagation
- Options of Gradient Descent
- Different Types of Deep Networks
5. Convolutional Neural Networks (CNN)
- An introduction to Convolutional Neural Networks
- Applications of the Convolutional Neural Networks
- Architecture of Convolutional Neural Networks
- Pooling and Convolutional layers in the Convolutional Neural Networks
- Visualizing the Convolutional Neural Networks
- Fine-tuning and transfer learning Convolutional Neural Networks
6. Recurrent Neural Networks (RNN)
- An introduction to Recurrent Neural Networks
- Applying use cases of Recurrent Neural Networks
- Modelling sequences of Recurrent Neural Networks
- Training RNNs with Backpropagation
- Long and short-term memory (LSTM)
- Theory of Neural Tensor Network
- Different Models of Recurrent Neural Network
7. Restricted Boltzmann Machine (RBM) and Autoencoders
- An overview of Restricted Boltzmann Machine
- Different applications of RBM
- Combined Filtering with RBM
- An overview of Autoencoders
- Applications of Autoencoders
- Understanding of Autoencoders
8. An Introduction to Keras
- An overview of Keras
- Ways to create models in Keras
- Functional and Sequential Compositions
- Predefined Neural Network Layers
- Batch Normalization: What exactly it is?
- Saving and loading the models with Keras
- Customization of the training process
- Uses of TensorBoard with Keras
- Process of Use-Case Implementation with Keras
9. An Introduction to TFlearn
- An overview of TFlearn
- Composing models in TFlearn
- Functional and Sequential Compositions
- Predefined Neural Network Layers
- Batch Normalization: What exactly it is?
- Saving and loading the models with TFlearn
- Customization of the training process
- Uses of TensorBoard with TFlearn
- Process of Use-Case Implementation with TFlearn