Overview
Deep Learning dives deeper into Machine Learning and can be thought of as a subset of Machine Learning. Neural networks allow computers to mimic the human brain. Just like our brain has the innate capability to recognize patterns that allow categorizing and classifying information, neural networks achieve the same for computers.
Deep Learning does incorporate deep neural networks due to the numerous layers of nested hierarchy of decision trees as millions of data points. Deep Learning leverages Natural Language Processing (NLP) and deep neural networks to establish insights facilitating effective decision-making.
Cognixia envisions the high influence of the cognitive and emerging technologies in the future. Deep Learning being one of the integral parts of the Industry 4.0 trends necessitates itself to be on the portfolio of every aspiring professional.
Curriculum
- What is deep learning and how it is different from Machine Learning
- Deep Learning – Usecases
- Packages & Libraries available for implementing Deep learning
- Where does Deep Leaning fit into Data Science Ecosystem
- Quick Review of Machine Learning
- How Deep Learning Works?
- Activation Functions
- Illustrate Perceptron
- Training a Perceptron
- Important Parameters of Perceptron
- What is Tensorflow?
- Tensorflow code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Creating a Model
- Step by Step – Use-Case Implementation
- Understand limitations of A Single Perceptron
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand Backpropagation – Using Neural Network Example
- MLP Digit-Classifier using TensorFlow
- TensorBoard
- Summary
- Installation & setup Python IDE – Anaconda
- NumPy
- SciPy
- Pandas,
- Matplotlib
- SciKit-Learn
- NLTK
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Convolution and Pooling layers in a CNN
- Understanding and Visualizing a CNN
- Transfer Learning and Fine-tuning Convolutional Neural Networks
- Intro to RNN Model
- Application use cases of RNN
- Modeling sequences
- Training RNNs with Backpropagation
- Long Short-Term memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
- Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Define Keras
- How to compose Models in Keras
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization
- Saving and Loading a model with Keras
- Customizing the Training Process
- Using TensorBoard with Keras
- Use-Case Implementation with Keras
- Define TFlearn
- Composing Models in TFlearn
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization
- Saving and Loading a model with TFlearn
- Customizing the Training Process
- Using TensorBoard with TFlearn
- Use-Case Implementation with TFlearn
- Deep Learning AMIs available
- Image Rekognition API
- Common Practise to setup Deep Learning Project in cloud
FAQs
