Deep Learning (DL)
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.Another often cited benefit of deep learning models is their ability to perform automatic feature extraction from raw data, also called feature learning.
Our Course Content
Introduction
- Introduction
- Why Should I Learn Deep Learning
Introduction to Neural Network
- what is neural network..?
- How neural networks works?
- Gradient descent
- Stochastic Gradient descent
- Perceptron
- Multilayer Perceptron
- BackPropagation
Building Deep learning Environment
- Overview of deep learning
- DL environment setup locally
- Installing Tensorflow
- Installing Keras
- Setting up a DL environment in the cloud
- AWS
- GCP
- Run Tensorflow program on AWS cloud plateform
Tenserflow Basics
- Placeholders in Tensorflow
- Defining placeholders
- Feeding placeholders with data
- Variables,
- Constant
- Computation graph
- Visualize graph with Tensor Board
Activation Functions
- What are activation functions?
- Sigmoid function
- Hyperbolic Tangent function
- ReLu -Rectified Linear units
- Softmax function
Training Neural Network for MNIST dataset
- Exploring the MNIST dataset
- Defining the hyperparameters
- Model definition
- Building the training loop
- Overfitting and Underfitting
- Building Inference
LEARNING ( Word Representation Using word2vec)
- Learning word vectors
- Loading all dependencies
- Preparing the text corpus
- defining our word2vec model
- Training the model
- Analyzing the model
- Visualizing the embedding space by plotting the model on tensorboard
LEARNING ( Clasifying Images with Convolutional Neural Networks(CNN) )
- Introduction to CNN
- Train a simple convolutional neural net
- Pooling layer in CNN
- Building ,training and evaluating our first CNN
- Model performance optimization
LEARNING ( Popular CNN Model Architectures )
- Introduction to Imagenet
- LeNet architecture
- AlexNet architecture
- VGGNet architecture
- ResNet architecture
LEARNING ( Introduction to Recurrent Neural Networks(RNN) )
- What are Recurrent Neural Networks (RNNs)?
- Understanding a Recurrent Neuron in Detail
- Long Short-Term Memory(LSTM)
- Back propagation Through Time(BPTT)
- Implementation of RNN in Keras
LEARNING ( HandWritten Digits and letters Classification Using CNN )
- Code Implementation
- Importing all of the dependencies
- Defining the hyperparameters
- Building a simple deep neural network
- Convolution in keras
- Pooling
- Dropout technique
- Data augmentation