TensorFlow and Keras Basics

TensorFlow, AI, machine learning, deep learning, neural networks, data science, model deployment, industry applications, TensorFlow training

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MODE
Online/ e-LMS

TYPE
Mentor Based

LEVEL
Beginners

DURATION
5 weeks

About

TensorFlow – Use in AI is a comprehensive course tailored for M.Tech, M.Sc, and MCA students, as well as professionals in the fields of IT, BFSI, consulting, and fintech. The course covers foundational concepts to advanced applications of TensorFlow in AI, emphasizing hands-on learning and real-world problem-solving.

Aim

This course aims to provide a deep understanding of TensorFlow, equipping participants with the skills to build, train, and deploy sophisticated machine learning models using TensorFlow.

Program Objectives

Comprehensive TensorFlow Mastery: In-depth knowledge of TensorFlow and its capabilities in AI development.
Practical Application Skills: Hands-on experience in building, training, and deploying AI models with TensorFlow.
Innovation in AI: Skills to innovate and solve complex problems using TensorFlow in various industry contexts.

Program Structure

Module 1: Introduction to TensorFlow and Keras

Section 1.1: Introduction to TensorFlow
- Subsection 1.1.1: What is TensorFlow?
  - Overview of TensorFlow as a deep learning framework.
  - Benefits of using TensorFlow: Flexibility, scalability, and performance.
- Subsection 1.1.2: Installing TensorFlow
  - Installing TensorFlow on various platforms (Windows, macOS, Linux).
  - Setting up TensorFlow in Jupyter notebooks and Google Colab.
- Subsection 1.1.3: TensorFlow Architecture
  - Understanding tensors and operations in TensorFlow.
  - Basics of TensorFlow computational graph and sessions.
Section 1.2: Introduction to Keras
- Subsection 1.2.1: What is Keras?
  - Keras as a high-level API for building deep learning models.
  - Differences between Keras and TensorFlow.
  - Why Keras simplifies TensorFlow.
- Subsection 1.2.2: Installing Keras
  - Installing Keras and TensorFlow together.
  - Setting up Keras in a local environment or cloud platform.
- Subsection 1.2.3: Keras Workflow
  - Understanding Keras’ simple and user-friendly interface.
  - Overview of Keras models: Sequential and Functional API.
Module 2: Core Concepts of TensorFlow and Keras

Section 2.1: Tensors in TensorFlow
- Subsection 2.1.1: Understanding Tensors
  - What is a tensor? Types of tensors in TensorFlow.
  - Operations on tensors: addition, multiplication, and reshaping.
  - TensorFlow tensor objects vs. NumPy arrays.
- Subsection 2.1.2: Basic TensorFlow Operations
  - Creating tensors in TensorFlow: tf.constant(), tf.Variable().
  - Performing operations on tensors: reshaping, slicing, and broadcasting.
Section 2.2: Building a Basic Neural Network with Keras
- Subsection 2.2.1: Keras Sequential Model
  - Overview of the Sequential model.
  - Creating a simple neural network with one hidden layer.
  - Adding activation functions (ReLU, sigmoid, etc.) to layers.
- Subsection 2.2.2: Compiling the Model
  - Understanding the model compilation step: loss functions, optimizers, and metrics.
  - Commonly used optimizers: SGD, Adam, RMSprop.
  - Common loss functions: Mean Squared Error (MSE), Binary Cross-Entropy, Categorical Cross-Entropy.
- Subsection 2.2.3: Model Training and Evaluation
  - Training the model using model.fit().
  - Evaluating model performance with model.evaluate().
  - Understanding training metrics (accuracy, loss).
Module 3: Advanced Techniques with TensorFlow and Keras

Section 3.1: Model Overfitting and Regularization
- Subsection 3.1.1: Overfitting in Neural Networks
  - What is overfitting, and how it impacts model performance.
  - Symptoms of overfitting and underfitting in machine learning models.
- Subsection 3.1.2: Techniques to Prevent Overfitting
  - Regularization methods: L2 Regularization, L1 Regularization.
  - Dropout layer: Understanding the concept and how to use it in Keras.
Section 3.2: Convolutional Neural Networks (CNNs) in Keras
- Subsection 3.2.1: Introduction to CNNs
  - Understanding Convolutional layers, pooling layers, and fully connected layers.
  - Architecture of a simple CNN for image classification.
- Subsection 3.2.2: Building a CNN with Keras
  - Defining convolutional layers with Conv2D.
  - Using MaxPooling2D for spatial reduction.
  - Adding dropout layers and dense layers for classification.
- Subsection 3.2.3: Model Training and Evaluation
  - Training CNN models on image datasets (e.g., CIFAR-10, MNIST).
  - Evaluating CNN models on accuracy and loss metrics.
Section 3.3: Recurrent Neural Networks (RNNs) in Keras
- Subsection 3.3.1: Introduction to RNNs
  - The architecture of Recurrent Neural Networks.
  - Understanding how RNNs are suited for sequential data (e.g., time series, text).
- Subsection 3.3.2: Building RNNs with Keras
  - Using SimpleRNN and LSTM layers for sequential data.
  - Understanding the difference between RNN, LSTM, and GRU.
- Subsection 3.3.3: Training and Evaluating RNN Models
  - Training RNN models on sequential data (e.g., time-series forecasting, text generation).
  - Evaluating model performance using metrics like accuracy and loss.
Module 4: Model Evaluation and Fine-Tuning

Section 4.1: Model Evaluation and Hyperparameter Tuning
- Subsection 4.1.1: Cross-validation Techniques
  - What is cross-validation?
  - Implementing K-fold cross-validation in TensorFlow.
  - Evaluating model stability using cross-validation.
- Subsection 4.1.2: Hyperparameter Tuning
  - Importance of tuning hyperparameters for model performance.
  - Manual vs. Automated hyperparameter tuning.
  - Grid search and random search using Keras and TensorFlow.
Section 4.2: Transfer Learning with Keras
- Subsection 4.2.1: What is Transfer Learning?
  - Overview of transfer learning and its benefits for deep learning tasks.
  - Using pre-trained models (e.g., VGG, ResNet, Inception) for new tasks.
- Subsection 4.2.2: Implementing Transfer Learning in Keras
  - Loading pre-trained models using tf.keras.applications.
  - Fine-tuning the pre-trained model for specific tasks (e.g., image classification).

Intended For

Students and professionals in M.Tech, M.Sc, and MCA, especially those in IT, BFSI, consulting, and fintech sectors.
Anyone interested in advancing their knowledge and skills in AI with TensorFlow.

Program Outcomes

Advanced TensorFlow Skills: Ability to utilize TensorFlow for advanced AI model development and deployment.
Strategic Implementation: Skills to strategically deploy TensorFlow models to solve real-world problems.
Leadership in AI: Enhanced capabilities to lead AI projects and initiatives using TensorFlow.

Mentors

AI Mentor

Rajnish Tandon

Bodhi Nexus (Founder)

Biography

AI Mentor

Pratish Jain

Rajiv Gandhi Proudyogiki Vishwavidyalaya

Biography

AI Mentor

J. T. Sibychen

Cyber and Cloud Security Trainer

NIIT Foundation

Biography

More Mentors

Fee Structure

Fee: INR 14,998 USD 214

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Key Takeaways

Access to e-LMS
Real Time Project for Dissertation
Project Guidance
Paper Publication Opportunity
Self Assessment
Final Examination
e-Certification
e-Marksheet

Placement Assistance

Industry Partnerships: Collaboration with leading companies using TensorFlow, providing insights and potential career opportunities.
Career Development: Workshops and guidance on career paths in AI development using TensorFlow.
Networking Events: Opportunities to engage with industry experts and fellow AI professionals.

Future Career Prospects

AI Engineer: Specializing in building and deploying AI models using TensorFlow.
Data Scientist: Leveraging TensorFlow for complex data analysis and machine learning solutions.
AI Product Manager: Overseeing AI product development and implementation strategies using TensorFlow.