3 Days Live Virtual Training on AI and Deep Learning with Python
About Artificial Intelligence (AI) with Python Course
The AI and Deep Learning with Python Certification course enables you to take your lastest skills like AI and Deep Learning into a variety of companies, helping them to apply these techniques on the data and make more informed business decisions. The course covers predictive analytics techniques with the Python language. You will learn about various Python packages like Tensorflow and Keras. This will give you a deep understanding on algorithms like Artificial Neural Networks, Convolutional Neural Networks and Recurrent neural networks.
Artificial Intelligence (AI) with Python Course Objective
Install Python, Jupyter Notebook,and learn about the various Python packages
Gain an in-depth understanding of data structure used in Python and learn to import/export data in Python
Define, understand and use the various functions in Python
Learn Python packages like Tensorflow and Keras
Learn indepth knowledge on AI and Deep learning algorithms like ANN, CNN and RNN and its various use cases.
Who is the Artificial Intelligence (AI) with Python Course Audience?
This course is meant for all those students and professionals who are interested in using the Python's powerful ecosystem
What Basic Knowledge Required to Learn Artificial Intelligence (AI) with Python Course?
There are no prerequisites
Introduction to Python
Introduction to Logistic Regression
Introduction to Artificial Neural Network
History of Neural networks and Deep Learning
How Biological Neurons work?
Growth of biological neural networks
Diagrammatic representation: Logistic Regression and Perceptron
Multi-Layered Perceptron (MLP)
Notation
Training a single-neuron model
Training an MLP: Chain Rule
Training an MLP:Memoization
Backpropagation
Activation functions
Vanishing Gradient problem
Bias-Variance tradeoff
Deep Multi-layer perceptrons
Deep Multi-layer perceptrons:1980s to 2010s
Dropout layers & Regularization
Rectified Linear Units (ReLU)
Weight initialization
Batch Normalization
Optimizers:Hill-descent analogy in 2D
Optimizers:Hill descent in 3D and contours
SGD Recap
Batch SGD with momentum
Nesterov Accelerated Gradient (NAG)
Optimizers:AdaGrad
Optimizers : Adadelta andRMSProp
Adam
Which algorithm to choose when?
Gradient Checking and clipping
Softmax and Cross-entropy for multi-class classification
How to train a Deep MLP?
Convolutional Neural Network
Biological inspiration: Visual Cortex
Convolution:Edge Detection on images
Convolution:Padding and strides
Convolution over RGB images
Convolutional layer
Max-pooling
CNN Training: Optimization
Receptive Fields and Effective Receptive Fields
ImageNet dataset
Data Augmentation
Convolution Layers in Keras
AlexNet
VGGNet
Residual Network
Inception Network
What is Transfer learning
Recurrent Neural Network
Why RNNs?
Recurrent Neural Network
Training RNNs: Backprop
Types of RNNs
Need for LSTM/GRU
LSTM
GRUs
Deep RNN
Bidirectional RNN