DEEP LEARNING

About Deep Learning

Deep learning is a type of machine learning that involves training artificial neural networks on a large dataset. Deep learning algorithms use multiple layers of artificial neurons to learn and make decisions, and are able to learn and improve over time through the process of training.

One of the key characteristics of deep learning is that it is able to automatically learn features and patterns in data, rather than requiring them to be manually defined by a programmer. This makes it particularly well-suited for tasks such as image and speech recognition, natural language processing, and predictive modeling.

Deep learning algorithms are typically implemented using neural networks, which are modeled after the structure and function of the human brain. Neural networks consist of layers of artificial neurons, which are connected together and trained using a large dataset. As the neural network is trained, it is able to learn and recognize patterns and relationships in the data, and can make predictions or decisions based on those patterns.

Deep learning has achieved a number of impressive results in recent years, and has been used to solve a wide range of problems, including image and speech recognition, natural language processing, and predictive modeling. It is an active area of research in the field of artificial intelligence, and is expected to continue to have a significant impact in the coming years.

Usage of Deep Learning

Deep learning is a type of machine learning that is particularly well-suited for tasks that involve large amounts of data and complex patterns. Some common areas where deep learning is used include:

1. 1. Image and speech recognition:  Deep learning algorithms are able to learn and recognize patterns in images and audio data, making them well-suited for tasks such as image and speech recognition.


2. 2. Natural language processing:  Deep learning algorithms can be used to process and understand human language, making them useful for tasks such as language translation, text classification, and sentiment analysis.


3. 3. Predictive modeling:  Deep learning algorithms can be used to make predictions based on patterns and relationships in data. This makes them useful for tasks such as stock market prediction, weather forecasting, and disease diagnosis.


4. 4. Robotics:  Deep learning algorithms can be used to enable robots to learn and make decisions based on sensory data, such as images and audio.


5. 5. Healthcare:  Deep learning algorithms can be used to analyze medical images, predict patient outcomes, and assist with diagnosis.

Overall, deep learning has a wide range of applications in areas such as image and speech recognition, natural language processing, predictive modeling, robotics, and healthcare. It is an active area of research and is expected to continue to have a significant impact in the coming years.

Scope of deep learning

Deep learning is a rapidly growing field with a wide range of potential applications. Some of the areas where deep learning is expected to have a significant impact in the coming years include:

1. 1. Artificial intelligence:  Deep learning algorithms are an important part of the field of artificial intelligence, and are expected to continue to be a key enabling technology for advances in this area.


2. 2. Autonomous vehicles:  Deep learning algorithms can be used to enable autonomous vehicles to make decisions based on sensory data, such as images and audio.


3. 3. Natural language processing:  Deep learning algorithms are expected to continue to improve the ability of machines to understand and process human language, leading to advances in areas such as language translation and text classification.


4. 4. Predictive modeling:  Deep learning algorithms are expected to continue to be used for predictive modeling tasks, such as stock market prediction and disease diagnosis.


5. 5. Healthcare:  Deep learning algorithms are expected to have a significant impact in the healthcare industry, enabling advances in areas such as medical image analysis and patient diagnosis.


6. 6. Robotics:  Deep learning algorithms are expected to enable robots to learn and make decisions based on sensory data, leading to advances in areas such as manufacturing and logistics.

Overall, the scope of deep learning is vast and is expected to continue to have a significant impact in a wide range of industries in the coming years. It is an active area of research and development, and is likely to continue to evolve and expand in the future.

Applications of Deep learning

Deep learning is a type of machine learning that involves using artificial neural networks with many layers to learn and make decisions. It has many applications, including:

1. 1. Image and speech recognition:  Deep learning is often used for image and speech recognition tasks, as it can learn to recognize patterns and features in data that are not easily visible to humans.


2. 2. Natural language processing:  Deep learning is used for tasks such as language translation and text classification, as it can learn to understand the complex relationships and contexts involved in human language.


3. 3. Predictive modeling:  Deep learning can be used to build predictive models that can learn from large amounts of data and make accurate predictions about future events.


4. 4. Personalization:  Deep learning can be used to personalize user experiences, such as recommending products or content based on an individual's preferences.


5. 5. Healthcare:  Deep learning is being used in healthcare to assist in the diagnosis and treatment of diseases, and to predict patient outcomes.


6. 6. Robotics:  Deep learning is being used to enable robots to perform tasks that require a high level of dexterity and situational awareness, such as assembling products or navigating unfamiliar environments.

Overall, deep learning has many applications and is being used in a wide range of industries to automate tasks, improve efficiency, and augment human capabilities.

Career Opportunity after Deep Learning

Deep learning is a subfield of machine learning that utilizes neural networks to analyze and understand large and complex datasets. There are many career opportunities in deep learning, including:

1. 1. Deep learning engineer:  This role involves designing, developing, and implementing deep learning models and algorithms.


2. 2. Data scientist:  A data scientist uses deep learning to analyze and extract insights from large and complex datasets.


3. 3. Computer vision engineer:  These engineers use deep learning to develop systems that can understand and interpret visual data, such as images and videos.


4. 4. Natural Language Processing (NLP) engineer:  This role uses deep learning to develop systems that can understand and interpret human language.


5. 5. Research scientist:  Researchers in deep learning focus on developing new deep learning algorithms and models, and advancing the state of the art in the field.


6. 6. Robotics Engineer:  Robotics engineers use deep learning to develop intelligent systems that can perceive, reason, and act autonomously.


7. 7. Autonomous systems engineer:  This role involves developing deep learning models for autonomous vehicles and other systems that operate independently.


8. 8. Industry-specific roles:  Many industries, such as finance, healthcare, and retail, are utilizing deep learning to improve their operations and services.

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Course Syllabus

• Syllabus
• Duration
• Download Syllabus
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• •   Introduction to Neural Network
  • what is neural network..?
  • How neural networks works?
  • Gradient descent
  • Stochastic Gradient descent
  • Perceptron
  • Multilayer Perceptron
  • BackPropagation
• •   Building Deep learning Environment
  • Overview of deep learning
  • DL environment setup locally
  • Setting up a DL environment in the cloud
  • Run Tensorflow program on AWS cloud plateform
• •   Tenserfow Basics
  • Placeholders in Tensorflow
  • Variables
  • Constant
  • Computation graph
  • Visualize graph with Tensor Board
• •   Activation Functions
  • What are activation functions?
  • Sigmoid function
  • Hyperbolic Tangent function
  • ReLu -Rectified Linear units
  • Softmax function
• •   Training Neural Network for MNIST dataset
  • Exploring the MNIST dataset
  • Defining the hyperparameters
  • Model definition
  • Building the training loop
  • Overfitting and Underfitting
  • Building Inference
• •   Word Representation Using word2vec
  • Learning word vectors
  • Visualizing the embedding space by plotting the model on tensorboard
• •   Clasifying Images with Convolutional Neural Networks(CNN)
  • Introduction to CNN
  • Train a simple convolutional neural net
  • Pooling layer in CNN
  • Building ,training and evaluating our first CNN
  • Model performance optimization
• •   Popular CNN Model Architectures
  • Introduction to Imagenet
  • LeNet architecture
  • AlexNet architecture
  • VGGNet architecture
  • ResNet architecture
• •   Introduction to Recurrent Neural Networks(RNN)
  • What are Recurrent Neural Networks (RNNs)?
  • Understanding a Recurrent Neuron in Detail
  • Long Short-Term Memory(LSTM)
  • Back propagation Through Time(BPTT)
  • Implementation of RNN in Keras
• •   Sequence-to-Sequence Models for Building Chatbot
• •   HandWritten Digits and letters Classification Using CNN
  • Code Implementation

TYPE	DURATION	HOURS	TRAINING MODE
Regular Track	45-60 Days	2 Hours a Day	Live Classroom
Weekend Track	8 Weekends	3 Hours a Day	Live Classroom
Fast Track	7 Days	6+ Hours a Day	Live Classroom

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