TensorFlow is a famous deep learning framework, this library is based on Python and will help you to run various algorithms of Artificial Neural network. Prerequisite: Participants must have knowledge of Python, knowledge of Machine learning will be helpful.
Data and Data Science.
Why Big Data.
Math and Data Science.
Introduction to Statistics.
What is learning?
Different type of learning.
Introduction to Data mining, machine learning.
Introduction to artificial intelligence.
What is a model?
NumPy Refresher :
Introduction to NumPy.
addition, subtraction, multiplication on Array
Pyplot as submodule.
Introduction to Jupyter.
TensorFlow with Jupyter.
Introduction to tensor in context of tensor flow.
TensorFlow Data types
Computation and Dataflow graph
Concept of session.
Mathematical operations in TensorFlow
Complex number operations.
Some more mathematical functions.
Matrix operation and Linear algebra in TensorFlow
Matrix summation and Substraction.
Determinant of Matrix.
Introduction to linear regression.
Simple linear regression.
Simple linear regression with TensorFlow.
Evaluating our model.
Logistic Regression Introduction.
Introduction to Clustering
Kmeans with TensorFlow
Why I use deep learning ?
Introduction to Neural Network
Biological Neuron an Introduction.
Component of biological Neuron.
Working of artificial neuron.
◦ Sigmoid function.
Concept of feed forward.
AND, OR and NOT
Perceptron learning algorithm.
Implementing Perceptron in TensorFlow.
Concept of gradient descent.
Problem of vanishing gradient.
MLP with TensorFlow.
Classifying our data.
Convolutional Neural networks (CNN)
Convolutional Neural networks Introduction.
Pooling Layer .
Image classification and Convolutional Networks.
TensorFlow and CNN
Image Classification with TensorFlow.
Recurrent Neural network (RNN)
Back Propagation through time (BPTT)
Need of Memory.
Long Short Term memory (LSTM).
Implementing RNN with TensorFlow.
Time Series and RNN
Sequence prediction with RNN.
Three Projects on Image classifications
One Project on time series with RNN
One Project on sequence prediction
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