Deep Learning Course

A branch of machine learning called "Deep Learning" use artificial neural networks to model intricate correlations and patterns in massive datasets. The idea of deep learning is motivated by the structure and operation of the human brain, and it has been applied to address a variety of issues, from speech and picture identification to natural language processing.

A neural network used for deep learning is made up of several linked layers of nodes, each of which processes the results of the layer before it. The neural network is trained using a lot of data, and it keeps revising its internal weights and biases based on the outcomes of this training in order to gradually boost its performance.

Skills You Will Learn:

• Understanding of artificial neural networks and their architecture.
• Knowledge of supervised, unsupervised, and reinforcement learning techniques.
• Experience with training and testing deep learning models using large datasets.
• Proficiency in using popular deep learning frameworks such as TensorFlow, Keras, and PyTorch.
• Ability to implement deep learning models for various applications, such as image and speech recognition, natural language processing, and computer vision.
• Understanding of hyperparameter tuning and model optimization techniques.
• Knowledge of common deep learning architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
• Understanding of transfer learning and fine-tuning pre-trained models.
• Ability to analyse and interpret the results of deep learning models.
• Knowledge of ethical and societal implications of deep learning.

Syllabus:

• I. Introduction to Deep Learning
• Overview of deep learning and its applications
• History and development of deep learning
• Advantages and disadvantages of deep learning
• Basic concepts of artificial neural networks
• II. Neural Networks Fundamentals
• Activation functions
• Loss functions
• Optimization algorithms
• Backpropagation algorithm
• III. Convolutional Neural Networks (CNNs)
• Architecture of CNNs
• Convolutional and pooling layers
• Image classification using CNNs
• IV. Recurrent Neural Networks (RNNs)
• Architecture of RNNs
• Long Short-Term Memory (LSTM) cells
• Text classification using RNNs
• V. Transfer Learning
• Overview of transfer learning
• Fine-tuning pre-trained models
• Transfer learning in computer vision and natural language processing
• VI. Deep Learning Frameworks
• Overview of popular deep learning frameworks
• TensorFlow
• Keras
• PyTorch
• VII. Deep Learning in Practice
• Case studies and real-world examples of deep learning
• Best practices and ethical considerations in deep learning
• Future trends and developments in deep learning