Deep learning with TensorFlow Python

• Data and Data Science.
• Big Data.
• 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?
• Mathematical models.

• Introduction to NumPy.
• Ndarray.
• Array creation
• Matrix
• addition, subtraction, multiplication on Array
• Matrix multiplication.
• MatPlotlib Refresher

• Scatterplot
• lineplot
• histogram
• PiChart
• Bar Chart
• Pandas Refresher

• TensorFlow History.
• Installing TensorFlow.
• Introduction to Jupyter.
• TensorFlow with Jupyter.
• Introduction to tensor in context of tensor flow.
• TensorFlow Data types
• Computation and Dataflow graph
• Concept of session.
• Constant
• Placeholder
• Variables.

• Multiplication
• Summation
• Maximum
• Minimum
• Complex number operations.
• Some more mathematical functions.

• Matrix summation and Substraction.
• Matrix Transpose.
• Determinant of Matrix.
• Matrix multiplication.
• Inverse matrix.
• Linear regression

• Simple linear regression.
• Parameter estimations.
• Simple linear regression with TensorFlow.
• Evaluating our model.

• Logistic Regression Introduction.
• Parameter estimation.
• With TensorFlow.
• Model Evaluation.
• Clustering

• Kmeans
• Kmeans with TensorFlow
• Optimizing Kmeans
• Market Segmentation.

• Introduction
• Use cases
• Why I use deep learning ?

Introduction to Neural Network

• Biological Neuron an Introduction.
• Component of biological Neuron.
• Artificial Neuron.
• Working of artificial neuron.
• Activation function
• Sigmoid function.
• Linear
• ReLU
• Tanh
• Concept of feed forward.
• AND, OR and NOT
• Perceptron.
• Perceptron learning algorithm.
• Implementing Perceptron in TensorFlow.

Multilayer perceptron

• Concept of gradient descent.
• Backpropgation algorithm.
• Problem of vanishing gradient.
• MLP with TensorFlow.
• Classifying our data.

• Convolutional Neural networks Introduction.
• Convolutional Layer.
• Pooling Layer .
• Connecting fully.
• Image classification and Convolutional Networks.
• TensorFlow and CNN
• Image Classification with TensorFlow.
• Model evaluation

• Introduction
• Back Propagation through time (BPTT)
• Need of Memory.
• Long Short Term memory (LSTM).
• Bi-Directional RNN
• Word embeding
• 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