The TensorFlow Specialty certification primarily acknowledges one's proficiency in developing AI and machine learning models using TensorFlow, a leading deep learning framework developed by Google. It is all about validating an individual's skills to deploy TensorFlow's tools for creating robust AI applications, from data-driven insights to predictive analytics. Industries leverage this certification to develop their AI teams, ensuring they are equipped with the necessary TensorFlow knowledge to handle large-scale machine learning tasks. The application of this certification can be seen across several sectors, from IT and healthcare to finance, for solving complex business problems, accelerating innovations, or delivering personalized customer experiences.

Course Objectives:

In this course, you will learn to:

• Understand TensorFlow concepts, functions, operations and the execution pipeline.
• Understand neural networks, deep learning algorithms, and data abstraction layers.
• Master advanced topics including convolutional neural networks, deep neural networks, recurrent neural networks, and high-level interfaces.
• Learn how to build deep learning models in TensorFlow and interpret the results
• Understand the fundamental concepts of artificial neural networks
• Learn the concepts of Google TensorFlow
• Understand Supervised and unsupervised learning
• Understand Backpropagation in neural networks
• Learn about convolutional neural networks
• Understand image recognition and natural language processing.

Course content

Introduction To Deep Learning

• Deep Learning: A revolution in Artificial Intelligence
• Limitations of Machine Learning
• Discuss the idea behind Deep Learning
• Advantage of Deep Learning over Machine learning
• 3 Reasons to go Deep
• Real-Life use cases of Deep Learning
• Scenarios where Deep Learning is applicable

The Math behind Machine Learning: Linear Algebra

• Scalars
• Vectors
• Matrices
• Tensors
• Hyperplanes

The Math Behind Machine Learning: Statistics

• Probability
• Conditional Probabilities
• Posterior Probability
• Distributions
• Samples vs Population
• Resampling Methods
• Selection Bias
• Likelihood

Review of Machine Learning Algorithms

• Regression
• Classification
• Clustering
• Reinforcement Learning
• Underfitting and Overfitting
• Optimization
• Convex Optimization

Fundamentals Of Neural Networks

• Defining Neural Networks
• The Biological Neuron
• The Perceptron
• Multi-Layer Feed-Forward Networks
• Training Neural Networks
• Backpropagation Learning
• Gradient Descent
• Stochastic Gradient Descent
• Quasi-Newton Optimization Methods
• Generative vs Discriminative Models

Activation Functions

• Linear
• Sigmoid
• Tanh
• Hard Tanh
• Softmax
• Rectified Linear
• Loss Functions
• Loss Function Notation
• Loss Functions for Regression
• Loss Functions for Classification
• Loss Functions for Reconstruction
• Hyperparameters
• Learning Rate
• Regularization
• Momentum
• Sparsity

Fundamentals Of Deep Networks

• Defining Deep Learning
• Defining Deep Networks
• Common Architectural Principals of Deep Networks
• Reinforcement Learning application in Deep Networks
• Parameters
• Layers
• Activation Functions – Sigmoid, Tanh, ReLU
• Loss Functions
• Optimization Algorithms
• Hyperparameters
• Summary

Introduction To TensorFlow

• What is TensorFlow?
• Use of TensorFlow in Deep Learning
• Working of TensorFlow
• How to install Tensorflow
• HelloWorld with TensorFlow
• Running a Machine learning algorithms on TensorFlow

Convolutional Neural Networks (CNN)

• Introduction to CNNs
• CNNs Application
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• Understanding and Visualizing a CNN
• Transfer Learning and Fine-tuning Convolutional Neural Networks

Recurrent Neural Networks (RNN)

• Introduction to RNN Model
• Application use cases of RNN
• Modelling sequences
• Training RNNs with Backpropagation
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model

Restricted Boltzmann Machine(RBM) And Autoencoders

• Restricted Boltzmann Machine
• Applications of RBM
• Collaborative Filtering with RBM
• Introduction to Autoencoders
• Autoencoders applications
• Understanding Autoencoders
• Variational Autoencoders
• Deep Belief Network

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