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Deep Learning with TensorFlow

Master neural networks, deep learning techniques, and the TensorFlow framework.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Explore the capabilities of Deep Learning with TensorFlow, a leading framework for AI and machine learning.

  2. This course offers an in-depth introduction to constructing and training deep neural networks.

  3. Topics include feedforward networks, convolutional networks, and recurrent networks.

  4. Acquire practical skills in developing and implementing models for applications such as image recognition and natural language processing.

  5. Engage in real-world projects, including time-series analysis, to enhance your learning experience.

CERTIFICATION:

  1. Earn a Certified Deep Learning Practitioner with TensorFlow credential, showcasing your expertise in developing and deploying deep learning models.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp the core principles of deep learning and neural networks.

  2. Develop, train, and assess deep learning models utilizing TensorFlow and Keras.

  3. Create convolutional neural networks (CNNs) for tasks such as image classification and object detection.

  4. Employ recurrent neural networks (RNNs) and LSTMs for processing sequential data, including text and time-series.

  5. Leverage TensorFlow’s advanced capabilities for custom model creation and optimization, and deploy models for real-time use in web and mobile applications.

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Course Curriculum

Introduction to Deep Learning
  1. What is Deep Learning?
    • Evolution from Machine Learning to Deep Learning.
    • Applications of Deep Learning: Image recognition, NLP, autonomous vehicles, etc.
  2. Overview of TensorFlow
    • What is TensorFlow, and why use it for Deep Learning?
    • Installation and environment setup.
TensorFlow Basics
  1. TensorFlow Fundamentals
    • Understanding tensors: Shapes, ranks, and data types.
    • Tensor operations and broadcasting.
  2. TensorFlow Workflows
    • Building and running computation graphs.
    • Using the TensorFlow tf.data API for data pipelines.
Building Neural Networks
  1. Introduction to Neural Networks
    • Layers, activation functions, and forward/backpropagation.
  2. Using Keras with TensorFlow
    • Building sequential models with Keras.
    • Adding dense layers and activation functions.
  3. Customizing Models
    • Creating functional and subclassed models.
Training Deep Learning Models
  1. Compiling Models
    • Loss functions: Mean squared error, cross-entropy, etc.
    • Optimizers: SGD, Adam, RMSprop.
  2. Model Training
    • Fitting models with model.fit().
    • Using validation data during training.
  3. Model Evaluation
    • Evaluating accuracy, precision, recall, and F1 score.
    • Testing on unseen data.
Convolutional Neural Networks (CNNs)
  1. Understanding CNNs
    • How CNNs work: Filters, strides, and pooling.
    • Applications of CNNs in image processing.
  2. Building CNNs
    • Using Conv2D, MaxPooling2D, and Flatten layers.
    • Image classification with CNNs.
  3. Transfer Learning
    • Leveraging pre-trained models (VGG, ResNet, etc.).
    • Fine-tuning for specific tasks.
Recurrent Neural Networks (RNNs)
  1. Understanding RNNs
    • Sequence modeling and temporal data.
    • Vanishing gradient problem and LSTMs/GRUs.
  2. Building RNNs
    • Using LSTM and GRU layers.
    • Applications: Text generation, time-series forecasting.
Advanced Topics in Deep Learning
  1. Autoencoders
    • Understanding and building autoencoders.
    • Applications in anomaly detection and image denoising.
  2. Generative Adversarial Networks (GANs)
    • Architecture of GANs: Generator and Discriminator.
    • Creating simple GANs for image generation.
  3. Attention Mechanisms and Transformers
    • Understanding attention in deep learning.
    • Introduction to transformer models for NLP.
Real-World Projects
  1. End-to-End Projects
    • Image classification: Dog vs. Cat classifier.
    • Time-series forecasting: Stock price prediction.
    • Natural language processing: Text sentiment analysis.
  2. Capstone Project
    • Build, train, and deploy a deep learning model tailored to a specific use case.

Training Features

Hands-On Practice

Extensive coding exercises with TensorFlow and Keras.

Comprehensive Coverage

Covers foundational concepts to advanced topics like GANs and Transformers.

Practical Applications

Focused on industry-relevant tasks such as image recognition and sequence modeling.

Optimization Techniques

Includes practical tips for improving model performance and deployment.

Interactive Learning

Live coding sessions, quizzes, and projects for better retention.

Certification

Earn a professional certificate showcasing your TensorFlow expertise.

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