Deep Learning using Python Certification TrainingDeep learning is a machine learning technique that clarifies computers to do what comes naturally to humans. In deep learning, a computer model studies how to perform classification jobs directly from images, text, or sound. The primary objective of deep learning is to create a deep neural network by enhancing and growing the number of training layers for each network; this helps the computer to gain more knowledge about the data till an exact data is achieved. Developers can learn these deep learning techniques to finish the complex machine learning tasks, and train AI networks to create the deep levels of perceptual recognition.
The deep learning course is designed for anyone with at least a year of coding experience, and some memory of college mathematics. This course intends to help you develop your capabilities in using deep learning to solve real-world problems. You will start with python programming tailored for data science and slowly will be able to build on or use existing state-of-the-art deep learning networks! The course focuses on top-down approach with theory-as-and-when-required moto. We’ll emphasize both the basic algorithms and the useful tricks needed to get them to work well.
- Basic python programming skills
- Basic mathematics skills
- Basic knowledge of Machine learning fundamentals
Course ObjectiveAt the end of this online Deep Learnitg using Python course, you will be able to:
- Fundamentals of Deep Learning techniques
- Artificial Neural networks and their architecture
- Building and training the deep neural networks from scratch.
- Convolutional Neural Networks (CNN)
- How to construct your own CNN?
- Different optimization techniques to tune the learning of any neural network?
- Pointers to next frontiers in CNN and Deep Learning
Who should attend this training?This training is suitable for:
- The Students who are interested in machine learning.
- Professionals who want to utilize neural networks in their machine learning and data science techniques.
- Candidates who are having coding knowledge in Python.
Prepare for Certification!
Our training and certification program gives you a solid understanding of the key topics covered in Deep Learning with Python. In addition to boosting your income potential, getting certified in Deep Learning with Python demonstrates your knowledge of the skills necessary to be an effective professional. The certification validates your ability to produce reliable, high-quality results with increased efficiency and consistency.
Unit 1: Deep Learning Introduction
- Introduction to DL problems
- DL terminologies
- DL project workflow
- DL real life examples
Unit 2: Jupyter Notebook introduction
- Working with Jupyter notebooks
- Markdown and Code blocks
- Keyboard shortcuts
Unit 3: Python Basics
- Python syntax
- Basic data types
- Basic data structures
Unit 4: Python advanced
- Numpy Arrays
- Plotting using Matplotlib
- Pandas Dataframes
- Introduction to Keras
Unit 1: Artificial Neural Networks (ANN)
- 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
Unit 1: Convolutional Neural Networks (CNN)
- Image representation
- ConvNets or CNN
- Convolution Layer
- 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
Unit 1: Auto Encoders (AE)
- 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
Unit 1: Recurrent Neural Networks (RNN)
- 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
We do not have a standard certification process neither are we affiliated with any university for certifying our course. We provide only the course attendance/completion certificate.
- Instructor Led Live Training (ILLT) – In this mode students attend the Live online sessions as per the training schedule. Assignments and course materials access is provided using the LMS system. Students can also view the videos of the past sessions and post questions using the LMS system. Students can ask trainers question live during the session or offline using the LMS system. 24×7 access to Support is available.
- Instructor Led Video Training (ILVT) – In this mode students do not attend Live online sessions but learn from the Session video recordings. Assignments and course materials access is provided using the LMS system. Students can post questions offline for trainers using the LMS system. 24×7 access to Support is available.
- Self-Paced Video Training (SPVT) – Self-paced video training program is designed to learn at your own pace. Students are given a access to the LMS system and learn thru pre-recorded session videos. They access the assignments and materials thru the LMS system. 24×7 access to Support is available.
- Operating System: Windows XP or newer
- Browser: Internet Explorer 6.x or newer
- CPU: P350 MHz, recommended P500+ MHz
- Memory: 128 MB, recommended 256+ MB RAM
- Free Disk Space: 40 MB, recommended 200+ MB for content and recordings
- Internet Connection: 28.8 Kbps, recommended 128+ Kbps
- Monitor: 16 bit colors (high color)
- Other: Sound card, microphone, and speakers OR headset with microphone