user: What is Nasdaq?

assistant: The Nasdaq Stock Market (National Association of Securities Dealers Automated Quotations Stock Market) is an American stock exchange based in New York City. It is the most active stock trading venue in the US by volume, and ranked second on the list of stock exchanges by market capitalization of shares traded, behind the New York Stock Exchange. The exchange platform is owned by Nasdaq, Inc., which also owns the Nasdaq Nordic stock market network and several U.S.-based stock and options exchanges. According to a Gallup poll conducted in 2022, approximately 58% of American adults reported having money invested in the stock market, either through individual stocks, mutual funds, or retirement accounts.

user: What are some pro tips when cold calling?

assistant: Cold calling is more art than science. The primary mistake people make when cold calling is in defining what success looks like.  For most sales roles, the goal of a cold call is to establish rapport, not to book an order.  Think about it - when was the last time you purchased something by phone the first time you were contacted?  Odds are that several follow ups were required before you were ready to buy, no matter how qualified a buyer you appeared to be.  

Establishing rapport means getting your prospect to open up, because he/she has determined that you are worth the time to do so.  So the keys to establishing rapport are simple:  be courteous and respectful, provide appropriate context, and be OPEN.   Let's consider each of these individually.  First, courtesy and respect- after briefly introducing yourself, ask the prospect if now is a good time to talk for a few minutes?  We've all experienced the annoyance of a caller who dives right into the conversation immediately after hearing a "hello".  Be respectful of your prospect's schedule and give him/her an "out."  If it's not a good time, you're not going to have a successful call no matter what you try.  

Next, provide appropriate context.  Don't assume he/she knows nothing about what you are trying to sell. If that were the case, this prospect wouldn't be a very good target for you! Prepare a short introductory statement that can be delivered in a genuine way with a friendly tone.  For example, "I'm with ACME Mobile Window Repair and wanted to share some spring promotions with you - have you seen our trucks around your neighborhood?"  Setting context will help build rapport by providing a clear understanding of why you are calling, and what you hope to get out of the conversation.  Successful sales people are very comfortable with the fact that they are trying to sell something.  Own it, but do so in a low key and friendly way, and this will help lower the defenses of your prospect if he/she sounds guarded

Lastly, be open.  This means open to going off script, open to hearing about competition or open to negative impressions of what you're selling.  An open dialogue allows room for questions, both from you and from your prospect.     Think about an enjoyable conversation you've had lately.  Was it a natural back and forth, with questions posed and answers provided?  Did the questions take the conversation into a deeper dialogue?  A successful cold call is one that feels like a natural conversation about a topic of shared interest. If your prospect goes off on a tangent, don't redirect too soon, or better yet - ask a question - "I'm curious - What made you think of carpets when I mentioned windows?"  If he/she goes negative about your offering, ask another question  - "What in your experience has caused you to feel this way?"  

Cold calling is a skill that can improve with practice.  The more comfortable you are being courteous, setting context, and remaining open, the better your sales outcomes will be.   And it never hurts to smile - even over the phone!

user: Please give me a short bulleted list of the key components/architectures in a convolutional neural network.

assistant: <think>
A convolutional neural network consists of an input layer, hidden layers and an output layer. In any feed-forward neural network, any middle layers are called hidden because their inputs and outputs are masked by the activation function and final convolution. In a convolutional neural network, the hidden layers include layers that perform convolutions. Typically this includes a layer that performs a dot product of the convolution kernel with the layer's input matrix. This product is usually the Frobenius inner product, and its activation function is commonly ReLU. As the convolution kernel slides along the input matrix for the layer, the convolution operation generates a feature map, which in turn contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers.

Convolutional layers

Convolutional layers convolve the input and pass its result to the next layer. This is similar to the response of a neuron in the visual cortex to a specific stimulus. Each convolutional neuron processes data only for its receptive field. Although fully connected feedforward neural networks can be used to learn features and classify data, this architecture is generally impractical for larger inputs (e.g., high-resolution images), which would require massive numbers of neurons because each pixel is a relevant input feature. A fully connected layer for an image of size 100 × 100 has 10,000 weights for each neuron in the second layer. Convolution reduces the number of free parameters, allowing the network to be deeper. For example, using a 5 × 5 tiling region, each with the same shared weights, requires only 25 neurons. Using regularized weights over fewer parameters avoids the vanishing gradients and exploding gradients problems seen during backpropagation in earlier neural networks.

To speed processing, standard convolutional layers can be replaced by depthwise separable convolutional layers, which are based on a depthwise convolution followed by a pointwise convolution. The depthwise convolution is a spatial convolution applied independently over each channel of the input tensor, while the pointwise convolution is a standard convolution restricted to the use of 

Pooling layers
Convolutional networks may include local and/or global pooling layers along with traditional convolutional layers. Pooling layers reduce the dimensions of data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer. Local pooling combines small clusters, tiling sizes such as 2 × 2 are commonly used. Global pooling acts on all the neurons of the feature map. There are two common types of pooling in popular use: max and average. Max pooling uses the maximum value of each local cluster of neurons in the feature map, while average pooling takes the average value.

Fully connected layers
Fully connected layers connect every neuron in one layer to every neuron in another layer. It is the same as a traditional multilayer perceptron neural network (MLP). The flattened matrix goes through a fully connected layer to classify the images.

Receptive field
In neural networks, each neuron receives input from some number of locations in the previous layer. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's receptive field. Typically the area is a square (e.g. 5 by 5 neurons). Whereas, in a fully connected layer, the receptive field is the entire previous layer. Thus, in each convolutional layer, each neuron takes input from a larger area in the input than previous layers. This is due to applying the convolution over and over, which takes into account the value of a pixel, as well as its surrounding pixels. When using dilated layers, the number of pixels in the receptive field remains constant, but the field is more sparsely populated as its dimensions grow when combining the effect of several layers.

To manipulate the receptive field size as desired, there are some alternatives to the standard convolutional layer. For example, atrous or dilated convolution expands the receptive field size without increasing the number of parameters by interleaving visible and blind regions. Moreover, a single dilated convolutional layer can comprise filters with multiple dilation ratios, thus having a variable receptive field size.

Weights
Each neuron in a neural network computes an output value by applying a specific function to the input values received from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias (typically real numbers). Learning consists of iteratively adjusting these biases and weights.

The vectors of weights and biases are called filters and represent particular features of the input (e.g., a particular shape). A distinguishing feature of CNNs is that many neurons can share the same filter. This reduces the memory footprint because a single bias and a single vector of weights are used across all receptive fields that share that filter, as opposed to each receptive field having its own bias and vector weighting.</think>
The key components/architecture of a convolutional neural network include:

Convolutional layers: These layers convolve the input matrix and reduce the number of free parameters and allows the network to be deeper.

Pooling layers: These layers reduce the dimensions of data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer.

Fully connected layers: These layers connect every neuron in one layer to every neuron in another layer. 

Receptive field: Each neuron receives input from a restricted area of the previous layer called the neuron's receptive field. In a convolutional layer, each neuron takes input from a larger area in the input than previous layers.

Weights: The vectors of weights and bias are used by the neural's function to compute an output value from input value.