Deep Learning Essentials
Designed for ambitious professionals seeking to master the foundations of deep learning, this program blends essential neural concepts, practical coding, and real-world applications to equip participants with the skills to build and apply advanced AI models in today’s evolving landscape.
Language
English
4.9 (By 6 Learners )
Why Bakkah?
Money Guaranteed
Global Accreditation
Flexible Learning
About this Course
What to Expect From This Deep Learning Essentials Course?
By the end, participants will be able to:
- Understand the structure and function of neural networks.
- Apply activation functions, layers, and loss functions effectively.
- Utilize TensorFlow and Keras for building AI models.
- Develop Convolutional Neural Networks (CNNs) for image classification tasks.
- Explore Recurrent Neural Networks (RNNs) for sequence-based data.
- Integrate deep learning models into practical business and technical solutions.
Who Should Enroll in this Deep Learning Essentials Course?
- Data scientists and AI enthusiasts are aiming to deepen their technical expertise.
- Software engineers and developers seeking to build deep learning applications.
- Professionals in IT and business looking to apply AI solutions in real-world contexts.
- Students and researchers interested in advanced machine learning concepts.
- Anyone aspiring to specialize in neural networks and deep learning.
What are the acquired skills from this Deep Learning Essentials Course?
- Designing and training neural networks.
- Applying activation and loss functions in deep learning models.
- Building AI solutions using TensorFlow and Keras.
- Developing CNNs for computer vision tasks.
- Implementing RNNs for sequential and time-series data.
- Translating deep learning models into real-world business applications.
Course Inclusions
- Biological inspiration & artificial neurons
- Structure: input, hidden, output layers
- Weights, biases, and forward propagation
- Backpropagation explained step by step
- Activation functions overview
- Saudi use case: Arabic handwriting recognition
- Sigmoid, ReLU, Tanh, Softmax explained
- Layer types: fully connected, convolutional, recurrent
- Loss functions: MSE, cross-entropy
- Why loss functions matter
- Saudi healthcare example: diagnosing X-ray images
- Why frameworks? TensorFlow vs. PyTorch
- Installing TensorFlow/Keras
- Building your first neural network step by step
- Model compilation, fitting, and evaluation
- Hands-on example: classifying simple images
- Saudi/Gulf example: product demand prediction
- What CNNs are & why they work for images
- Convolution & filters explained
- Pooling layers and feature maps
- Building a CNN with Keras
- Case study: Facial recognition in Saudi airports
- Exercise: Build CNN for CIFAR-10 dataset
- Sequential data explained (time-series, text, speech)
- RNN structure & how it remembers past inputs
- Vanishing gradient problem & solutions (LSTM, GRU)
- Saudi example: predicting Tadawul stock prices
- Exercise: Build a simple RNN for text prediction
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