+91 9325761018 letsconnect@skillsmetrix.com Login for Student Login for Admin

Comprehensive Deep Learning with Python

Comprehensive Deep Learning with Python

Enroll Now
data-analyst-science near Pune
Recorded content
Of Total 10 Hrs.
data-analyst-science near Pune
Duration
3 Months (50 hours)
data-analyst-science near Pune
LIVE sessions
4 Workshops
data-analyst-science near Pune
Hands-On Learning
With Practice Modules
data-analyst-science near Pune
Certificate
With License

Overview

This hands-on, live Deep Learning with Python training course builds on our Comprehensive Data Science with Python class and teaches attendees the fundamentals of Deep Learning, and to implement artificial neural network (ANN) applications, using Keras and TensorFlow.

Objective

  • Learn the fundamental theory behind neural networks
  • Model an arbitrary function using an artificial neural network (ANN)
  • Practice interpreting loss metrics and convergence conditions
  • Apply a neural net to a regression problem
  • Understand regularization within the context of ANNs
  • Implement dropout and LASSO as network regularization strategies
  • Apply Deep Learning to a classification problem
  • Implement image processing methods in Python and Keras
  • Extend feed-forward network architectures to convolutional layers
  • Construct 2D convolutional image classification architectures
  • Perform a multi class classification
  • Apply Deep Learning to sequential data using recurrent architectures (RNNS, LSTMs and GRUs)
  • Apply Deep Learning to time series forecasting applications
  • Automate ANN architecture selection using Autokeras
  • Understand the concept of Latent Semantic Representations and word embeddings
Join Next Batch Starting on September 10th

Outline

  • • Why artificial neural networks? Advantages of ANNs
  • • Understanding the essential concepts
  • • Activation functions, optimizers, back-propagation
  • • Components and architectures of artificial neural networks
  • • Evaluate the performance of neural networks on a known function
  • • Define and monitor convergence of a neural network
  • • Model selection
  • • Scoring new datasets with a model
  • • Preprocessing structured datasets for Deep Learning workflows
  • • Model validation strategies
  • • Architectural modifications to manage generalization error
  • • Regularization strategies
  • • Deep Learning: regression models
  • • Deep Learning: classification models

  • • Management and preparation of image data for Deep Learning models
  • • The dimensionality of image data
  • • Handling image metadata
  • • Conversion of images to NumPy arrays
  • • Python Image Library (PIL) and skimage
  • • Keras' load_img() function
  • • Image standardization and resampling
  • • Augmentation strategies for image data

  • • Image data is multidimensional
  • • Overview of convolutional architectures
  • • Convolution layers act as filters
  • • Pooling layers reduce computation
  • • Data augmentation through image transformation for smaller datasets
  • • Image transformation using the pillow library
  • • Applying a model to a multi class labeled dataset
  • • Evaluating a confusion matrix for multiple classes

  • • Identify limitations of feed-forward ANN architectures for sequential data
  • • Modify model architecture to include recurrent (RNN) components
  • • Preprocessing time series data for ingestion into RNN models
  • • Examine improvements to RNNs: The LSTM and GRU networks
  • • Time series forecasting with recurrent architectures
  • • Time series forecasting with 1D convolutional architectures

  • • Text manipulation with TensorFlow
  • • Categorical representations and word embeddings
  • • Text embeddings as layers in an ANN
  • • Word2vec
  • • Exploiting pre-trained word embedding models
  • • Visualizing semantic relationships between words using t-SNE

  • • Exploiting pre-trained models (VGG16) for image classification
  • • Selecting layers to unlock for specific applications
  • • Transfer learning and fine tuning

  • Generative AI fundamentals
    • • What is an autoencoder?
    • • Building a simple autoencoder from a fully connected layer
    • • Sparse autoencoders
    • • Deep convolutional autoencoders
    • • Applications of autoencoders to image denoising
    • • Sequential autoencoders
    • • Variational autoencoders

  • • Adversarial examples
  • • Generational and discriminative networks
  • • Building a simple generative adversarial network
  • • Generating images with a GAN

  • • The problems with recurrent architectures for sequential data
  • • Attention-based architectures
  • • Positional encoding
  • • The Transformer: attention is all you need
  • • Time series classification using transformers
  • • GPT-3 and the future of natural language generation
  • • Open AI Codex and the future of programmatic code generation

Training Materials

All Deep Learning training students receive comprehensive courseware.

Software Requirements

• Windows, Mac, or Linux with at least 8 GB RAM

• A current version of Anaconda for Python 3.x

• Related lab files that Skillsmetrix will provide

Why Online Bootcamps

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Learn by working on real-world problems

Capstone projects involving real world data sets with virtual labs for hands-on learning

Learn from experts active in their field, not out-of-touch trainers

Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.

Structured guidance ensuring learning never stops

24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts