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2nd Mar 2019

3 Days

Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday

6th Apr 2019

3 Days

Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday

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.

**Introductory Terms**

- 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.

**NumPy Refresher :**

- Introduction to NumPy.
- Ndarray.
- Array creation
- Matrix
- addition, subtraction, multiplication on Array
- Matrix multiplication.

**MatPlotlib Refresher**

- Pyplot as submodule.
- Scatterplot
- lineplot
- histogram
- PiChart
- Bar Chart

**Pandas Refresher**** **

- DataFrame
- Dataframe operations

**TensorFlow Introduction**

- 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.

**Mathematical operations in TensorFlow**

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

**Matrix operation and Linear algebra in TensorFlow**

- Matrix summation and Substraction.
- Matrix Transpose.
- Determinant of Matrix.
- Matrix multiplication.
- Inverse matrix.

**Linear regression**

- Introduction to linear regression.
- Simple linear regression.
- Parameter estimations.
- Simple linear regression with TensorFlow.
- Evaluating our model.

**Logistic Regression **

- Logistic Regression Introduction.
- Parameter estimation.
- With TensorFlow.
- Model Evaluation.

**Clustering**

- Introduction to Clustering
- Kmeans
- Kmeans with TensorFlow
- Optimizing Kmeans
- Market Segmentation.

**Deep Learning**

- 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 (CNN)**

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

**Recurrent Neural network (RNN)**

- 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.

**Projects :**

- Three Projects on Image classifications
- One Project on time series with RNN
- One Project on sequence prediction