This carefully crafted Deep Learning Course Syllabus is designed to provide you with a comprehensive understanding of deep learning concepts, methodologies, and applications. From neural networks to convolutional neural network model architecture and recurrent neural networks, you’ll explore cutting-edge techniques in machine learning that power advancements in various industries. Whether you’re a fresher or an experienced practitioner, our Deep Learning Course Syllabus offers hands-on exercises and projects to develop your skills and unleash the full potential of deep learning algorithms. So, enroll in our Deep Learning Training
Deep Learning Course Syllabus
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Deep Learning Course 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
- Installing Tensorflow
- Installing Keras
- Setting up a DL environment in the cloud
- AWS
- GCP
- Run Tensorflow program on AWS cloud plateform
Tenserfow Basics
- Placeholders in Tensorflow
- Defining placeholders
- Feeding placeholders with data
- 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
- Loading all dependencies
- Preparing the text corpus
- defining our word2vec model
- Training the model
- Analyzing the model
- 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
HandWritten Digits and letters Classification Using CNN
- Code Implementation
- Importing all of the dependencies
- Defining the hyperparameters
- Building a simple deep neural network
- Convolution in keras
- Pooling
- Dropout technique
- Data augmentation
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