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Course Description
This course delves into the world of deep learning using Python, one of the most versatile and widely-used programming languages in the field of data science and artificial intelligence. The course is designed to provide participants with a strong foundation in deep learning concepts, techniques, and applications, using Python's powerful libraries and frameworks.
What you'll learn in this course?
At the end of Deep Learning with Python training course, participants will learn
• Fundamentals of Deep Learning techniques
• Artificial Neural networks and their architecture
• Building and training the deep neural networks from scratch.
• Convolutional Neural Networks (CNN)
• Different optimization techniques to tune the learning of any neural network
• Pointers to next frontiers in CNN and Deep Learning
Prerequisites
1. Basic python programming skills
2. Basic mathematics skills
3. Basic knowledge of Machine learning fundamentals
Course Curriculum
• Introduction to DL problems
• DL terminologies
• DL project workflow
• DL real life examples
• Working with Jupyter notebooks
• Markdown and Code blocks
• Keyboard shortcuts
• Python syntax
• Basic data types
• Basic data structures
• Numpy Arrays
• Plotting using Matplotlib
• Pandas Dataframes
• Introduction to Keras
• What is a Neuron
• What are Activation Functions
• How does a neural network learn?
• Gradient Descent
• Stochastic Gradient Descent
• Back Propagation
• Artificial Neural Networks in Keras
• Linear model (No Hidden Layers)
• Neural network with a single hidden layer
• Image representation
• ConvNets or CNN
• Convolution Layer
• Padding
• How do we learn these kernels?
• Can we force a particular kernel to learn to recognize a specific feature?
• Non-Linear Activations
• Downsampling or Pooling
• Full Connection
• Loading MNIST data
• Implementation of CNN in Keras
• Introduction to Auto Encoders
• Why learn identity function?
• Properties of learned function
• Real world applications of Autoencoders
• MNIST Dimensionality Reduction
• A Simple Autoencoder
• Functional API
• Deep Autoencoder
• Convolutional Autoencoder
• Image Denoising
• Data Specific Encoding and Decoding
• Introduction to Recurrent Neural Networks
• Sequence Learning
• Regular Neural Network
• Simple RNN
• Problems with RNN
• Long Short Term Memory (LSTM)
• Stacked (Deep) LSTM Model
• Deep Stacked LSTM with Stateful Cells
• Gated Recurrent Unit
LEARN AT YOUR OWN PACE
Training Options
Discover our range of training programs and choose the ones that suit you best. Enroll today and begin your learning journey with us!
