Students Trained


Course Duration

40+ Hours

Real Project's


New Batch Starting

9th Jun 2019

Discount Offer

Now Fee: ₹ 6000/- ₹ 5000/-

Submit your fee on or before 25th May 2019 and get discount of Rs 1000/-. Registration Fees to Submit is Rs 1000/-

Pending Fees of Rs 4000/- to be paid when you join the batch

GROUP DISCOUNT: For group payment of min. 4 students Get 10% Discount (per head)

Key features
  • 40+ hours of instructor-led training.
  • 5+ projects During Training.
  • Certification from Technex'19 of IIT BHU.
  • Module wise projects.
What is Deep Learning with TensorFlow Course about?
  • Deep Learning with TensorFlow course has been crafted by industry experts and aligned with the latest best practices to transform you into deep learning expert.
  • You’ll learn to master deep learning concepts and the TensorFlow open source framework, implement deep learning algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for an exciting career in deep learning.

Frequently asked questions

Deep Learning is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. This deep learning course with TensorFlow is designed to help you master deep learning techniques and build deep learning models using TensorFlow, the open-source software library developed by Google for the purpose of conducting machine learning and deep neural networks research. It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks

1. Machine Learning Engineers
2. Data Analyst
3. Data Scientist
4. Anyone who wants to add Deep Learning with TensorFlow skills to their profile
5. Teams getting started on Deep Learning with TensorFlow projects

1. Basic Programming knowledge in Python
2. Fundamental level understanding of Machine Learning

Course Content

Step - 1
Introduction of Machine Learning/Deep learning and Artificial Intelligent
  • Introduction of Artificial Intelligence and Machine Learning
  • Introduction of Deep Learning
  • Difference between Machine Learning and DeepLearning
  • Types of Learning
  • Supervised Learning-Regression Classification
Step - 2
Linear Algebra,Statistics
  • Array , Metrix, Metrix operation
  • Eigen Value, Eigen Vector, orthogonality
  • Mean, Median, Mode
  • Variance and Standard Deviation
  • Probability, Probability Density Functions
  • Normal & Gaussian Distribution
  • Bayes' Theorem
  • Bernoulli & Binomial Distribution
  • Vector, Dot product
Step - 3
Basic Python and Anaconda
  • Introduction to python and anaconda
  • Conditional Statements
  • Looping, Control Statements
  • Lists, Tuple ,Dictionaries
  • Functions
  • Introduction of Anaconda
  • Working on Jupyter notebook
Step - 4
Working on Various Python Library
  • Matplotlib, Seaborn
  • Scipy and Numpy
  • Pandas
  • scikit-learn
Step - 5
Introduction of Deep Learning Algorithms
  • Gradient Descent
  • Perceptrons
  • Neural Network
  • RNN
  • LSTM
  • CNN
  • Application of Various Deep Learning Algorithms
Step - 6
Neural Network
  • BASIC introduction Neuron
  • The Neuron Diagram
  • Neuron Models
  • Activation function
  • Linear Function
  • single-layer feed-forward
  • multi-layer feed-forward
  • Feedforward Neural Networks
Step - 7
Working with TensorFlow
  • Introduction of Tensorflow
  • Basics of TensorFlow
  • Graph in TensorFlow
  • TensorFlow Session
  • Placeholders,Constants,Variables
  • Common Data Stored in Tensors
Step - 8
  • What is RNN?
  • How to train a RNN
  • Long Term Dependencies
  • LSTM Cell
  • GRU Cell
  • Convolutional Neural Network (CNN)
  • What is CNN?
  • CNN Architecture
  • Pooling and Stride
Step - 9
Hand on Projects
  • Object Detection using TensorFlow
  • RNN With TensorFlow
  • Time Series using RNN
  • MNIST Using CNN'
  • CIFAR Image Dataset
  • Facial Expression Recognition Project
  • Images classification Using TensorFlow
  • Feeding Data to the Training Algorithm
  • Visualizing the Graph and Training Curves Using TensorBoard

Programming Language and Tools we Used


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