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Deep Learning with TensorFlow

  • Course level: All Levels

Description

Self-Paced, Live Online & Classroom Enterprise Training

Master core concepts of Deep Learning with Google’s TensorFlow- a distributed scalable deep learning platform. Our Deep Learning course aids in building Deep Learning Models and Applications in TensorFlow, suitable for different business domains. Train your team on TensorFlow and Neural Nets to solve complex Organizational problems.

What Will I Learn?

  • The Deep Learning training enables you to:
  • Articulate the core architecture and API layers TensorFlow
  • Construct a computing environment and learn to install TensorFlow
  • Develop TensorFlow graphs required for everyday computations
  • Use logistic regression for classification along with TensorFlow
  • Develop, design and train a multilayer neural network with TensorFlow
  • Demonstrate Activation functions and Optimizers in detail with hands-on
  • Demonstrate intuitively convolutional neural networks for image recognition
  • Design and construct a neural network from simple to more accurate models
  • Understand recurrent neural networks, its applications and learn how to build these solutions
  • Understand hyper-parameters and tuning
  • Learn how to build industry's leading uses cases eg, Recommendation systems, Speech recognition, commercial grade Image classification and object localization etc....
  • Lead ML/DL projects based on TensorFlow implementation

Topics for this course

18h

Introduction?

Data science & its importance Key Elements of Data Science Artificial Intelligence & Machine Learning Introduction Who uses AI? AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain What makes a Machine Learning Expert? What to learn to become a Machine Learning Developer? Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization Deep Learning: A revolution in Artificial Intelligence What is Deep Learning? Advantage of Deep Learning over Machine learning 3 Reasons to go for Deep Learning Real-Life use cases of Deep Learning

Neural Networks Basics?

How Neural Networks Work? Various activation functions – Sigmoid, Relu, Tanh Perceptron and Multi-layer Perceptron What is TensorFlow? TensorFlow code-basics Graph Visualization Constants, Placeholders, Variables Creating a Model Step by Step - Use-Case Implementation Introduction to Keras Understand Neural Networks in Detail Illustrate Multi-Layer Perceptron Backpropagation – Learning Algorithm Understand Backpropagation – Using Neural Network Example MLP Digit-Classifier using TensorFlow

Deep Neural Networks?

Why Deep Networks Why Deep Networks give better accuracy? Understand How Deep Network Works? How Backpropagation Works? Illustrate Forward pass, Backward pass Different variants of Gradient Descent Types of Deep Networks Batch Normalization Activation and Loss functions Hyper parameter tuning Training challenges and techniques Optimizers, learning rate, momentum, etc.

Convolutional Neural Networks?

Introduction to CNNs CNNs Application Architecture of a CNN Forward propagation & Backpropagation for CNNs Convolution, Pooling, Padding & its mechanisms Understanding and Visualizing a CNN An overview of pre-trained models (AlexNet, VGGNet, InceptionNet & ResNet) and Transfer Learning Image classification using CNN

Advanced Computer Vision?

Auto encoders Semantic segmentation YOLO Siamese Networks Object & face recognition using techniques above

Natural Language Processing?

Sentiment Analysis Topic Summarization Topic Modelling Nltk, Gensim, vader, etc. Bag of Words and Tf-IDF Cosine Similarity of terms, documents concepts Text Cleaning and Preprocessing using Regex Tokenization, Stemming and Lemmatization

RNN And LSTM?

Introduction to Sequential data Word embeddings and lang translation RNNs and its mechanisms Vanishing & Exploding gradients in RNNs Time series analysis LSTMs LSTMs with attention mechanism GRU

Visualization Using Tensorboard?

What is Tensor board? Test vs Train set accuracy T-SNE Occlusion Experiment CAM, Saliency and Activation maps Visualizing Kernels Style transfer

Reinforcement Learning And Gans?

Introduction How GANs work? Applications of GANs (Generative adversarial networks)

About the instructor

5.00 (1 ratings)

79 Courses

12 students

Cloud Computing Essentials
Free

Material Includes

  • Enterprise Reporting
  • Lifetime Access
  • CloudLabs
  • 24x7 Support
  • Real-time code analysis and feedback
  • 100% Money Back Guarantee

Requirements

  • Basic Programming knowledge in Python
  • Fundamental level understanding of Machine Learning
  • Note: The above knowledge is must-have for the participants to fully appreciate the training content.
  • Suggested:
  • Knowledge of Deep Neural Network models
  • MNIST database

Target Audience

  • Machine Learning Engineers
  • Data Analyst
  • Data Scientist
  • Anyone who wants to add Deep Learning with TensorFlow skills to their profile
  • Teams getting started on Deep Learning with TensorFlow projects