AI Courses
Deep Learning
Launchpad’s Deep Learning Course provides specialized training in deep learning algorithms and techniques, with a focus on neural networks, convolutional networks, and recurrent networks.
Deep Learning Course
Launchpad’s Deep Learning Course provides specialized training in deep learning algorithms and techniques, with a focus on neural networks, convolutional networks, and recurrent networks. You’ll work with tools like TensorFlow, Keras, and PyTorch to build and train models. Our instructors guide you through hands-on projects that apply deep learning to complex problems. The course is available online and in-person and prepares you for a career in deep learning, along with certification and job placement support.
Curriculum
- what is neural network..?
- How neural networks works?
- Gradient descent
- Stochastic Gradient descent
- Perceptron
- Multilayer Perceptron
- BackPropagation
- 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
- Placeholders in Tensorflow
- Defining placeholders
- Feeding placeholders with data
- Variables,
- Constant
- Computation graph
- Visualize graph with Tensor Board
- What are activation functions?
- Sigmoid function
- Hyperbolic Tangent function
- ReLu -Rectified Linear units
- Softmax function
- Exploring the MNIST dataset
- Defining the hyperparameters
- Model definition
- Building the training loop
- Overfitting and Underfitting
- Building Inference
- 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
- Introduction to CNN
- Train a simple convolutional neural net
- Pooling layer in CNN
- Building ,training and evaluating our first CNN
- Model performance optimization
- Introduction to Imagenet
- LeNet architecture
- AlexNet architecture
- VGGNet architecture
- ResNet architecture
- 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
- Code Implementation
- Importing all of the dependencies
- Defining the hyperparameters
- Building a simple deep neural network
- Convolution in keras
- Pooling
- Dropout technique
- Data augmentation
Objectives of Learning Deep Learning
Launchpad’s Deep Learning course helps you:
- Master Deep Learning Concepts: Learn the foundations of deep learning and its applications.
- Work with Neural Networks: Gain experience with building and training neural networks.
- Use Advanced Tools: Work with TensorFlow, Keras, PyTorch, and other deep learning libraries.
- Apply Deep Learning: Develop models for tasks like image recognition, natural language processing, and time-series forecasting.
- Handle Large Datasets: Learn techniques for managing and processing large datasets in deep learning.
- Prepare for Certification: Get ready for deep learning certification exams.
Reason to Choose Launchpad for Deep Learning Training
Launchpad’s Deep Learning training offers:
- Authorized Certification: Obtain certifications from top tech companies.
- Experienced Instructors: Learn from industry professionals with deep learning expertise.
- Interactive Sessions: Participate in live, instructor-led training—available both online and offline.
- Hands-On Projects: Work on projects that mirror real-world deep learning challenges.
- Comprehensive Learning: Build technical expertise and soft skills, including communication and interview techniques.
- Job Placement Support: Benefit from placement assistance, including resume building and LinkedIn optimization.